Using R for Epidemiological Analysis

Transcription

1 Using R for Epidemiological Analysis Practical 3: Disease Mapping in R Introduction In this session we will use R to do some disease mapping. We will work through an example of how to create basic maps in R by creating a map of Estonia. We will then move on to an example of creating smoothed SMRs and plot them on a map by working with data on hospital admissions for chronic obstructive pulmonary disease (COPD) for England between All data required for this practical can be found in the folder Data. You will need the following files shapefiles and information for Estonia split by regions (Estonia.shp, Estonia.dbf, Estonia.prj, Estonia.shx) shapefiles and information for England split by local authorities (englandlocalauthority.shp, englandlocalauthority.dbf) observed numbers of hospital admissions by local authority (copdmortalityobserved.csv) expected numbers of hospital admissions by local authority (copdmortalityobserved.csv). Preliminaries We need the following packages spdep - Package to create spatial objects (such as neighbourhood matrix). shapefiles - Package to read and write shapefiles. CARBayes - Package to fit spatial GLMMs. rgdal - Package to handle spatial objects. rgeos - Package to handle spatial objects. As before we use the install.packages() function to download and install the packages that we need and the require() function to load them into the R library. install.packages("spdep") install.packages("shapefiles") install.packages("carbayes") install.packages("rgdal") install.packages("rgeos") require(spdep) require(shapefiles) require(carbayes) require(rgdal) require(rgeos) Before reading in any data for this practical you will need to ensure that you are in the correct folder. As explained in Practical, you can use the setwd() function

2 setwd("chosen_directory_path") If you cannot get the setwd() to work, go to Session > Set Working Directory > Choose Directory in the toolbar on the top. Creating an Estonia map To create maps, we need to read in the relevant shapefiles. The files Estonia.shp, Estonia.dbf, Estonia.prj and Estonia.shx contain the location, shape, and attributes of Estonia by region. The boundaries (shapefiles) were obtained from A map of Estonia split by more local authories can also be downloaded from here. The function readogr() will read these shapefiles into R. Estonia <- readogr(dsn=.,layer= Estonia ) OGR data source with driver: ESRI Shapefile Source: ".", layer: "Estonia" with 6 features It has 2 fields To check that the shapefiles have been read into R correctly, we can plot the boundaries using the plot() function. plot(estonia) We use simulated random numbers to create the map here. If you have your own data, we can use that. To plot this simulated data we use the ssplot() function. # Adding random numbers to each Estonia region Estonia@data$rnum <- rnorm(nrow(estonia@data),0,) # Cut off points for legend of plot range <- seq(min(estonia@data$rnum)-0.0, max(estonia@data$rnum)+0.0, length.out=5) # Creating Map spplot(obj = Estonia, # Spatial object to be plotted 2

3 zcol = c("rnum"), # Choice of the column the object you are plotting. xlab = "Easting", # Label of the x column ylab = "Northing", # Label of the y column at = range, # Break points for colours col.regions = hsv(0.6, seq(0.2,, length.out=0), )) # Create a set of colours.5.0 Northing Easting Reading in England Data and Shapefiles The observed and expected COPD counts in England by local authority need to be read into R. These are in csv format, so we use the read.csv() function. observed <- read.csv(file="copdmortalityobserved.csv", row.names=) expected <- read.csv(file="copdmortalityexpected.csv", row.names=) To check that the data has been read into R correctly, we can use the head() function, which prints the first six rows of a dataset. head(observed) name Y200 Y2002 Y2003 Y2004 Y2005 Y2006 Y AA City of London LB AB Barking and Dagenham LB AC Barnet LB AD Bexley LB AE Brent LB AF Bromley LB Y2008 Y2009 Y200 00AA 0 00AB AC AD

4 00AE AF head(expected) E200 E2002 E2003 E2004 E2005 E AA AB AC AD AE AF E2007 E2008 E2009 E200 00AA AB AC AD AE AF To familiarise ourselves with the data, we can summarise it using the summary() function. This will allow us to check for anomalies in our data. summary(observed) name Y200 Y2002 Y2003 Adur CD : Min. : 2.00 Min. : 0.00 Min. : 3.00 Allerdale CD : st Qu.: st Qu.: st Qu.: Amber Valley CD: Median : Median : Median : Arun CD : Mean : 68.0 Mean : Mean : Ashfield CD : 3rd Qu.: rd Qu.: rd Qu.: Ashford CD : Max. : Max. : Max. : (Other) :38 Y2004 Y2005 Y2006 Y2007 Min. :.00 Min. :.00 Min. :.00 Min. : 5.00 st Qu.: st Qu.: st Qu.: st Qu.: Median : Median : 5.00 Median : Median : Mean : Mean : Mean : Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.: Max. : Max. : Max. : Max. : Y2008 Y2009 Y200 Min. :.00 Min. : 0.00 Min. :.00 st Qu.: st Qu.: st Qu.: Median : 5.00 Median : Median : 5.00 Mean : 7.40 Mean : Mean : rd Qu.: rd Qu.: rd Qu.: 8.25 Max. : Max. : Max. :44.00 summary(expected) E200 E2002 E2003 E2004 Min. : Min. : 2.68 Min. : Min. : 2.75 st Qu.: st Qu.: st Qu.: st Qu.: Median : Median : Median : Median : Mean : Mean : Mean : Mean :

5 3rd Qu.: rd Qu.: rd Qu.: rd Qu.: Max. : Max. :37.27 Max. : Max. : E2005 E2006 E2007 E2008 Min. : Min. : 2.96 Min. : Min. : 3.5 st Qu.: st Qu.: st Qu.: st Qu.: Median : Median : Median : Median : Mean : Mean : Mean : Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.: Max. : Max. : Max. : Max. : E2009 E200 Min. : Min. : st Qu.: st Qu.: Median : Median : Mean : Mean : rd Qu.: rd Qu.: Max. : Max. : Does it look like R has read in the data correctly? 2. Are there any strange values in our dataset? To create SMR maps, we need to read in the relevant shapefiles. As we are going to attach the SMRs to the shapefiles to create maps, we use a di erent method to creating a spatial object than above. Instead we build the spatial object from scratch. The files englandlocalauthority.shp and englandlocalauthority.dbf contain the location, shape, and attributes of English local authorities. The functions read.shp() and read.dbf() will read shapefiles into R. shp <- read.shp(shp.name="englandlocalauthority.shp") shp$shp[[23]]$points <- shp$shp[[23]]$points[-c(460, 46, 462, 463, 464, 465), ] dbf <- read.dbf(dbf.name="englandlocalauthority.dbf") dbf$dbf <- dbf$dbf[,c(2,,3:7)] Modelling the Raw SMRs Now that we have read in the data, we can calculate raw SMRs. Remember that SMR_raw <- observed[,-]/expected SMR = observed expected To change attributes of a dataset such as the column names, we use the names() function. names(smr_raw) <- c("smr200", "SMR2002", "SMR2003", "SMR2004", "SMR2005", "SMR2006", "SMR2007", "SMR2008", "SMR2009", "SMR200") It is important that we check that no errors have occurred at any stages, so we check by summarising the results using the head() and summary() functions. 5

6 head(smr_raw) SMR200 SMR2002 SMR2003 SMR2004 SMR2005 SMR2006 SMR AA AB AC AD AE AF SMR2008 SMR2009 SMR200 00AA AB AC AD AE AF summary(smr_raw) SMR200 SMR2002 SMR2003 SMR2004 Min. : Min. : Min. :0.366 Min. : st Qu.: st Qu.: st Qu.:0.859 st Qu.: Median : Median :.068 Median :.0209 Median : Mean :.0349 Mean :.0508 Mean :.0895 Mean : rd Qu.: rd Qu.: rd Qu.:.307 3rd Qu.:.858 Max. :.986 Max. :2.28 Max. : Max. :.98 SMR2005 SMR2006 SMR2007 SMR2008 Min. : Min. : Min. : Min. :0.32 st Qu.: st Qu.:0.745 st Qu.: st Qu.: Median : Median :0.90 Median : Median : Mean :.026 Mean : Mean : Mean : rd Qu.: rd Qu.:.586 3rd Qu.:.679 3rd Qu.:.979 Max. :2.244 Max. : Max. :.8528 Max. : SMR2009 SMR200 Min. : Min. : st Qu.: st Qu.: Median : Median : Mean : Mean : rd Qu.: rd Qu.:.335 Max. :.8507 Max. : Does it look like the SMRs have been estimated correctly? 2. Are there any strange values? Mapping the Raw SMRs To plot these SMRs to a map, we need to attach them to the shapefiles. The function combine.data.shapefile() allows us to combine shapefiles to plot later. SMRspatial_raw <- combine.data.shapefile(data = SMR_raw, # Dataset to attach shp = shp, # Shapefile dbf = dbf) # Database file 6

7 Now that the estimates are attached to the shapefile, the function spplot() allows us to create a map which colours the local authorities by the SMR estimate. range <- seq(min(smr_raw$smr200)-0.0, max(smr_raw$smr200)+0.0, length.out=) spplot(obj = SMRspatial_raw, # Spatial object to be plotted zcol = c("smr200"), # Choice of the column the object you are plotting. xlab = "Easting", # Label of the x column ylab = "Northing", # Label of the y column at = range, # Break points for colours col = "transparent", # Choice of colour col.regions = hsv(0.6, seq(0.2,, length.out=0), )) # Create a set of colours 2.0 Northing Easting. What do you notice about this new plot? 2. Are there any strange values? 3. If so, do you believe that these are the truth or perhaps sampling error? Modelling the smoothed SMRs To calculate the smoothed SMRs, we first need to create a neighbourhood structure. poly2nb() and nb2mat() can be used to create this. The functions # Creates the neighbourhood W.nb <- poly2nb(smrspatial_raw, row.names = rownames(smrspatial_raw)) # Creates a matrix for following function call W.mat <- nb2mat(w.nb, style="b") Here, we use first neighbours to define our structure, so any local authority that shares a border with another are considered neighbours. 7

8 The function S.CARleroux() allows us to use this neighbourhood structure and performs a Bayesian analysis, to create a smoothed set of observed values as discussed in the lecture. model <- S.CARleroux(formula=observed$Y200~offset(log(expected$E200)), # Model Formula family="poisson", # Choosing Poisson Regression W=W.mat, # Neighbourhood matrix burnin=20000, # Number of burn in samples n.sample=00000, # Number of MCMC samples thin=0, fix.rho=true, rho=) The new smoothed values can be extracted from the model output and divided by the expected values to allow comparison between the two methods. SMR200 <- model$fitted.values / expected$e200 SMR_smooth <- as.data.frame(smr200, row.names = rownames(observed)) Again, we check that no errors have occurred, by summarising the results using the head() and summary() functions. head(smr_smooth) SMR200 00AA AB AC AD AE AF summary(smr_smooth) SMR200 Min. : st Qu.:0.793 Median :0.924 Mean : rd Qu.:.0802 Max. : Does it look like the SMRs have been estimated correctly? 2. Are there any strange values? Mapping the Smoothed SMRs Similarly to before, we attach the values of the smoothed SMRs to the shapefile using the combine.data.shapefile() function and create a map using the spplot() function. SMRspatial_smooth <- combine.data.shapefile(data = SMR_smooth, # Dataset to attach shp = shp, # Shapefile dbf = dbf) # Database file 8

9 spplot(obj = SMRspatial_smooth, # Spatial object to be plotted zcol = c("smr200"), # Choice of the column the object you are plotting. xlab = "Easting", # Label of the x column ylab = "Northing", # Label of the y column at = range, # Break points for colours col = "transparent", # Choice of colour col.regions = hsv(0.6, seq(0.2,, length.out=0), )) # Create a set of colours 2.0 Northing Easting. What do you notice about this new plot? 2. Are there any di erences between the smoothed and raw estimates? You may want to repeat this analysis for another year to see if the results are similar. Carefully go through the previous sections and change any references from 200 to any year that you wish between