diff --git a/.Rbuildignore b/.Rbuildignore deleted file mode 100644 index 1d00090..0000000 --- a/.Rbuildignore +++ /dev/null @@ -1,6 +0,0 @@ -^.*\.Rproj$ -^\.Rproj\.user$ -^\.travis\.yml$ -^netlify\.toml$ -^appveyor\.yml$ -^LICENSE\.md$ diff --git a/.Rprofile b/.Rprofile new file mode 100644 index 0000000..81b960f --- /dev/null +++ b/.Rprofile @@ -0,0 +1 @@ +source("renv/activate.R") diff --git a/.github/workflows/netlify.yaml b/.github/workflows/netlify.yaml new file mode 100644 index 0000000..67b3af4 --- /dev/null +++ b/.github/workflows/netlify.yaml @@ -0,0 +1,55 @@ +name: Test book render + +on: + pull_request: + branches: [main, master] + workflow_dispatch: + +jobs: + test-book-render: + runs-on: ubuntu-latest + container: rocker/tidyverse:4.4.2 + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v4 + + - name: Install system dependencies + run: | + apt-get update && apt-get install -y --no-install-recommends \ + libxt6 libglpk-dev gh curl jq + + - name: Set up Quarto + uses: quarto-dev/quarto-actions/setup@v2 + with: + tinytex: true + + - name: Install packages from renv.lock (with cache) + if: ${{ !env.ACT }} + uses: r-lib/actions/setup-renv@v2 + with: + cache-version: 2 + + - name: Install packages from renv.lock (local, no cache) + if: ${{ env.ACT }} + run: | + renv::restore() + shell: Rscript {0} + + - name: Render document + run: quarto render + + - name: Deploy to Netlify + uses: nwtgck/actions-netlify@v3.0 + with: + publish-dir: './_site' + production-branch: main + github-token: ${{ secrets.GITHUB_TOKEN }} + deploy-message: + 'Deploy from GHA: ${{ github.event.pull_request.title || github.event.head_commit.message }} (${{ github.sha }})' + # these default to 'true' + enable-commit-comment: false + enable-github-deployment: false + env: + NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} + NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }} \ No newline at end of file diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml new file mode 100644 index 0000000..5ed8943 --- /dev/null +++ b/.github/workflows/publish.yaml @@ -0,0 +1,38 @@ +on: + workflow_dispatch: + push: + branches: main + +name: Quarto Publish + +jobs: + build-deploy: + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Set up Quarto + uses: quarto-dev/quarto-actions/setup@v2 + + - name: Install tinytex + run: quarto install tinytex + + - name: Install R + uses: r-lib/actions/setup-r@v2 + with: + r-version: '4.4.2' + + - name: Install R Dependencies + uses: r-lib/actions/setup-renv@v2 + with: + cache-version: 1 + + - name: Render and Publish + uses: quarto-dev/quarto-actions/publish@v2 + with: + target: gh-pages + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file diff --git a/.gitignore b/.gitignore index 7b732e7..9257ca7 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,7 @@ .RData .Ruserdata .DS_Store + + +/_site/ +/.quarto/ diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 6e3e7ae..0000000 --- a/.travis.yml +++ /dev/null @@ -1,8 +0,0 @@ -# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r - -language: R -sudo: false -cache: packages - -script: - - Rscript -e 'bookdown::render_book("index.rmd")' \ No newline at end of file diff --git a/02-sampling.Rmd b/02-sampling.qmd similarity index 73% rename from 02-sampling.Rmd rename to 02-sampling.qmd index c46bfc7..f1122e9 100644 --- a/02-sampling.Rmd +++ b/02-sampling.qmd @@ -1,6 +1,6 @@ -# Sampling {#sampling} +# Sampling {#sec-sampling} -## The RAM-OP sample +## The RAM-OP sample {#sec-ram-op-sample} RAM-OP uses a two-stage sample: @@ -10,24 +10,23 @@ taken. A sampled community is also called a primary sampling unit (PSU). **Second stage sample:** Domestic dwellings are sampled from within the communities selected in the first stage sample. All eligible individuals in the sampled dwelling are included in the sample. -### The first-stage sample +### The first-stage sample {#sec-first-stage-sample} The first stage sample is a systematic spatial sample. Two methods can be used and both methods take the sample from all parts of the survey area: * **List-based method:** Communities to be sampled are selected systematically from a complete list of communities in the survey area. This list of communities is sorted by one or more non-overlapping spatial factors such as district and subdistricts within districts: -```{r sample1, echo = FALSE, fig.cap = "Communities listing by district and sub-district", out.width = "80%", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/listSample1.png") -``` +![Communities listing by district and sub-district](figures/listSample1.png){#fig-sample1 fig.alt="List of communities by district and sub-district" fig-align="center"} * **Map-based method:** Communities to be sampled are selected from the centres of the squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of every community in the survey area. This type of sample is known as a centric systematic area sample and is often referred to as a CSAS sample. **Note:** *Population proportional sampling* (PPS) is **not** used in RAM-OP surveys. Population estimates for all communities are **not** required for sampling purposes. Population estimates are required only for the selected communities. These are used during data analysis in order to weight results by population size. If this information is not available before the survey, it can be collected during the survey. -### The second stage sample +### The second stage sample {#sec-second-stage-sample} + The second stage within-community sample uses a method called map-segment-sample. This method takes the within-community sample from all parts of a sampled community. -## Implicit stratification +## Implicit stratification {#sec-implicit-stratification} Both the first and second stage samples use a form of spatial stratification: @@ -46,7 +45,7 @@ The use of implicit stratification improves the efficiency of a two-stage cluste \newpage -## RAM-OP survey sample size +## RAM-OP survey sample size {#sec-ram-op-sample-size} The following shorthand symbols will be used when describing sample designs: @@ -87,13 +86,11 @@ Do not be tempted to increase the size of the within-community sample in order t Here, for example, is a *population pyramid* for a typical developing country: -```{r sample2, echo = FALSE, fig.cap = "Population pyramid for a typical developing country", out.width = "90%", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/popPyramid1.png") -``` +![Population pyramid for a typical developing country](figures/popPyramid1.png){#fig-sample2 fig-alt="Population pyramid for a typical developing country" fig-align="center"} If the average community population is $N = 300$ then there will be fewer than 15 people aged 60 years and older in about half of the selected communities. This is because about half of the selected communities are likely to have a population below the average population. -## Eligibility +## Eligibility {#sec-eligibility} Older people are usual defined as persons aged 60 years and older (UN definition). This means your sample will usually be restricted to people aged 60 years and older. @@ -103,14 +100,11 @@ In a setting of very high life-expectancy you may want to restrict eligibility - In a setting with very low life-expectancy, very few people are aged 60 years or older. For example: -```{r sample3, echo = FALSE, fig.cap = "Population pyramid for a setting with low life-expectancy", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/popPyramid2.png") -``` +![Population pyramid for a setting with low life-expectancy](figures/popPyramid2.png){#fig-sample3 fig-alt="Population pyramid for a setting with low life-expectancy" fig-align="center"} -It is common in such setting for there to be a local definition of older people. This will usually be “persons -aged 50 years or older” or “persons aged 55 years or older”. +It is common in such setting for there to be a local definition of older people. This will usually be **“persons aged 50 years or older”** or **“persons aged 55 years or older”**. -## Age distribution, eligibility criteria, and sample design +## Age distribution, eligibility criteria, and sample design {#sec-age-distribution} The age distribution of the population and the survey eligibility criteria will affect the sample design in terms of the number of communities that you will need to sample ($m$) and the number of older persons ($n$) that can be sampled from each community. @@ -134,9 +128,9 @@ If this is below about 20 people then you should consider how you will collect t If the proportion of older people is not very small and / or communities are large then you should have no problems achieving the overall sample size. -## Practical sampling +## Practical sampling {#sec-practical-sampling} -### The first stage sample - list-based sampling +### The first stage sample - list-based sampling {#sec-first-stage-list-based} The first stage sample can be drawn from a list of all communities. The list-based sample is a simple systematic sample taken from a complete list of communities in the survey area sorted by one or more non- overlapping spatial factors (such as administrative units or electoral wards) in the survey area. *Population proportional sampling* (PPS) is not used since this would concentrate the sample in the larger communities. @@ -158,7 +152,7 @@ A random number can be selected through simple lottery (i.e., draw from a lot of   -```{r, eval = FALSE} +```R RANDBETWEEN(1, 4) ``` @@ -168,51 +162,50 @@ RANDBETWEEN(1, 4) \newpage -```{r sample4, echo = FALSE, fig.cap = "Selection of sampling villages using lists", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/listSample2.png") -``` +![Selection of sampling villages using lists](figures/listSample2.png){#fig-sample4 fig-alt="Selection of sampling villages using lists" fig-align="center"} \newpage -### The first stage sample - map-based sampling +### The first stage sample - map-based sampling {#sec-first-stage-map-based} An alternative approach to list-based sampling is to use map-based sampling. The map-based (CSAS) sample selects communities from the centre of squares of a grid drawn over a map. The map must be sufficiently well made and of sufficiently large scale to show the position of **all** communities in the survey area. -A square grid is drawn over the map. The size of the grid squares should be small enough so that the number of squares covering the survey area is the same as (or very similar to) the number of communities that you plan to sample. You may need to experiment with different grid sizes to achieve this. Figure \@ref(fig:sample6) shows an example map and grid with $m = 16$ grid squares. +A square grid is drawn over the map. The size of the grid squares should be small enough so that the number of squares covering the survey area is the same as (or very similar to) the number of communities that you plan to sample. You may need to experiment with different grid sizes to achieve this. @fig-sample6 shows an example map and grid with $m = 16$ grid squares. The sample is drawn by selecting the community that is located closest to the centre of each grid square: -```{r sample5, echo = FALSE, fig.cap = "Selection of sampling villages using maps", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/mapSample1.png") -``` +![Selection of sampling villages using maps](figures/mapSample1.png){#fig-sample5 fig-alt="Selection of sampling villages using maps" fig-align="center"} If two or more villages are located the same distance from the centre of a grid square then a single village is picked at random, by tossing a coin for example. -Figure \@ref(fig:sample7) shows the sample selected by this process for the area shown in Figure \@ref(fig:sample6). +@fig-sample7 shows the sample selected by this process for the area shown in @fig-sample6. -```{r sample6, echo = FALSE, fig.cap = "Drawing a square grid over the map", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/mapSample2.png") -``` +::: {#fig-csas layout-ncol=2} -```{r sample7, echo = FALSE, fig.cap = "Drawing the first-stage CSAS sample", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/mapSample3.png") -``` +![Drawing a square grid over the map](figures/mapSample2.png){#fig-sample6 fig-alt="Drawing a square grid over the map" fig-align="center"} + +![Drawing the first-stage CSAS sample](figures/mapSample3.png){#fig-sample7 fig-alt="Drawing the first-stage CSAS sample" fig-align="center"} + +Map-based sampling +::: Both the list-based and the map-based (CSAS) sampling methods spread the sample of communities evenly across the entire survey area. Each community has an equal chance of being included in the sample. Population proportional sampling (PPS) is not used since this would concentrate the sample in the larger communities. -The same method can be used when sampling in urban contexts. Figure \@ref(fig:sample8) shows a sample drawn from a list of census enumeration areas sorted by administrative district. Figure \@ref(fig:sample9) shows a sample drawn using the map- based (CSAS) method. In both cases the primary sampling units (PSUs) are census enumeration areas. +The same method can be used when sampling in urban contexts. @fig-sample8 shows a sample drawn from a list of census enumeration areas sorted by administrative district. @fig-sample9 shows a sample drawn using the map- based (CSAS) method. In both cases the primary sampling units (PSUs) are census enumeration areas. -```{r sample8, echo = FALSE, fig.cap = "Example of an urban sample (list-based)", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/mapSample4.png") -``` +::: {#fig-list-csas layout-ncol=2} + +![Example of an urban sample (list-based)](figures/mapSample4.png){#fig-sample8 fig-alt="Example of an urban sample (list-based)" fig-align="center"} + +![Example of an urban sample (map-based)](figures/mapSample5.png){#fig-sample9 fig-alt="Example of an urban sample (map-based)" fig-align="center"} + +List-based vs map-based sampling +::: -```{r sample9, echo = FALSE, fig.cap = "Example of an urban sample (map-based)", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/mapSample5.png") -``` **Note:** In this example twenty-one (21) blocks have been selected. It can be difficult to achieve exactly the number of blocks that you need when using this type of sample. It is best to select more rather than fewer blocks than you need Here we would take our sample as $n = 10$ individuals from $m = 21$ blocks (overall $n = 210$). -### The second stage (within-community) sample +### The second stage (within-community) sample {#sec-second-stage-sample} The second stage (within-community) sample uses a map-segment-sample approach: @@ -220,15 +213,11 @@ The second stage (within-community) sample uses a map-segment-sample approach: Here is an example of a ribbon of dwellings: -```{r sample10, echo = FALSE, fig.cap = "Example of a ribbon of dwellings", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample1.png") -``` +![Example of a ribbon of dwellings](figures/stage2sample1.png){#fig-sample10 fig-alt="Example of a ribbon of dwellings" fig-align="center"} Here is an example of a cluster of dwellings: -```{r sample11, echo = FALSE, fig.cap = "Example of a cluster of dwellings", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample2.png") -``` +![Example of a cluster of dwellings](figures/stage2sample2.png){#fig-sample11 fig-alt="Example of a cluster of dwellings" fig-align="center"} **Segment:** Divide the community into ribbon and cluster segments defined by the physical layout of the community being sampled. @@ -239,77 +228,59 @@ knitr::include_graphics("figures/stage2sample2.png") **Note:** If a small community is selected that is likely to have fewer than the required number of eligible persons then **all** eligible persons in that community are sampled by moving door-to-door. -### Mapping the community - single and multiple clusters +### Mapping the community - single and multiple clusters {#sec-mapping-community-clusters} Some communities consist of a single cluster of dwellings: -```{r sample12, echo = FALSE, fig.cap = "Example of a cluster of dwellings", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample2.png") -``` +![Example of a cluster of dwellings](figures/stage2sample2.png){#fig-sample12 fig-alt="Example of a cluster of dwellings" fig-align="center"} or a set of clusters of dwellings: -```{r sample13, echo = FALSE, fig.cap = "Example of a set of clusters of dwellings", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample3.png") -``` +![Example of a set of clusters of dwellings](figures/stage2sample3.png){#fig-sample13 fig-alt="Example of a set of clusters of dwellings" fig-align="center"} For communities (or parts of communities) structured in this way we use a sampling method called the **random walk**. -### Mapping the community - ribbon communities +### Mapping the community - ribbon communities {#sec-mapping-ribbon} Ribbon communities have dwellings arranged in a line: -```{r sample14, echo = FALSE, fig.cap = "Dwellings arranged in a line", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample1.png") -``` +![Dwellings arranged in a line](figures/stage2sample1.png){#fig-sample14 fig-alt="Dwellings arranged in a line" fig-align="center"} -or in a several lines: +or in several lines: -```{r sample15, echo = FALSE, fig.cap = "Dwellings arranged in several lines", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample4.png") -``` +![Dwellings arranged in several lines](figures/stage2sample4.png){#fig-sample15 fig-alt="Dwellings arranged in several lines" fig-align="center"} For communities (or parts of communities) structured in this way we use a sampling method called **systematic sampling**. -### Mapping the community - mixed communities +### Mapping the community - mixed communities {#sec-mapping-mixed} Some communities are a mixture of clusters and ribbons: -```{r sample16, echo = FALSE, fig.cap = "Mixture of clusters and ribbons", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample5.png") -``` +![Mixture of clusters and ribbons](figures/stage2sample5.png){#fig-sample16 fig-alt="Mixture of clusters and ribbons" fig-align="center"} For mixed communities we use a mixture of the **random walk** method (in the clusters) and **systematic sampling** (along the ribbons). **Segmentation** involves dividing a community into several parts and taking part of the within-community sample from each **segment**. With simple communities, segmentation is not required and we take a single sample from the entire community using the appropriate sampling method. -### Segmentation +### Segmentation {#sec-segmentation} For more complicated communities we divide the community into several parts or segments, such as a community made up of several clusters: -```{r sample17, echo = FALSE, fig.cap = "Example of a set of clusters of dwellings", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample3.png") -``` +![Example of a set of clusters of dwellings](figures/stage2sample3.png){#fig-sample17 fig-alt="Example of a set of clusters of dwellings" fig-align="center"} or a community made up of several ribbons: -```{r sample18, echo = FALSE, fig.cap = "Dwellings arranged in several lines", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample4.png") -``` +![Dwellings arranged in several lines](figures/stage2sample4.png){#fig-sample18 fig-alt="Dwellings arranged in several lines" fig-align="center"} or a mixed community: -```{r sample19, echo = FALSE, fig.cap = "Mixture of clusters and ribbons", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample5.png") -``` +![Mixture of clusters and ribbons](figures/stage2sample5.png){#fig-sample19 fig-alt="Mixture of clusters and ribbons" fig-align="center"} We take a small sample from each segment using the appropriate sampling method. For example, with a community made up of three segments: -```{r sample20, echo = FALSE, fig.cap = "Community made up of three segments", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample6.png") -``` +![Community made up of three segments](figures/stage2sample6.png){#fig-sample20 fig-alt="Community made up of three segments" fig-align="center"} we would take one third of the overall sample from each segment. @@ -323,15 +294,13 @@ Segments should be either ribbons or clusters but should **never** contain both A dwelling can only belong to one segment. Segments should **not** overlap. -### Sample dwellings +### Sample dwellings {#sec-sample-dwellings} **All** segments should be sampled. If, for example, there are five segments in a community: -```{r sample21, echo = FALSE, fig.cap = "Community made up of five segments", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample7.png") -``` +![Community made up of five segments](figures/stage2sample7.png){#fig-sample21 fig-alt="Community made up of five segments" fig-align="center"} and the within-community sample size is twelve eligible subjects, then you would plan to sample two eligible subjects from each segment (i.e. $12 / 5 = 2.4$ **rounded down** to two) and, if necessary, return to the **largest** segment to complete the sample. @@ -345,7 +314,7 @@ Remember that different types of segment are sampled in different ways: We will look at each of these sampling methods in turn. -### Random walk sampling +### Random walk sampling {#sec-random-walk-sampling} The **random walk** method is used to sample dwellings in **cluster segments**. Sampling proceeds as follows: @@ -363,11 +332,9 @@ If, when you have sampled all segments, you have not sampled twelve eligible per The random walk method is illustrated in Figure \@ref(fig:sample22). -```{r sample22, echo = FALSE, fig.cap = "Random walk sampling in a cluster segment", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample8.png") -``` +![Random walk sampling in a cluster](figures/stage2sample8.png){#fig-sample22 fig-alt="Random walk sampling in a cluster segment" fig-align="center"} -### Systematic sampling +### Systematic sampling {#sec-systematic-sampling} The **systematic sampling** method is used to sample houses in **ribbon segments**. @@ -389,56 +356,42 @@ If, when you have sampled all segments, you have not sampled twelve eligible per The systematic sampling method is illustrated in Figure \@ref(fig:sample23). -```{r sample23, echo = FALSE, fig.cap = "Systematic sampling in a ribbon segment", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample9.png") -``` +![Systematic sampling in a ribbon segment](figures/stage2sample9.png){#fig-sample23 fig-alt="Systematic sampling in a ribbon segment" fig-align="center"} -### Sampling in urban settings +### Sampling in urban settings {#sec-sampling-urban} In urban areas the first stage sample is taken by replacing sub-districts with “sections” and communities with city blocks. Examples of sections may be administrative districts/sub-districts or electoral wards. -```{r sample24, echo = FALSE, fig.cap = "Administrative divisions in an urban setting", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample10.png") -``` +![Administrative divisions in an urban setting](figures/stage2sample10.png){#fig-sample24 fig-alt="Administrative divisions in an urban setting" fig-align="center"} -Census enumeration areas (EAs) are usually city blocks. Central statistics offices can usually provide lists of EAs by “section” and large-scale maps of EAs selected for sampling (See Figure \@ref(fig:sample25) and Figure \@ref(fig:sample26)). These maps make it easy to locate EAs and their boundaries. The sample of EAs can be decided using list-based or map-based (CSAS) sampling. +Census enumeration areas (EAs) are usually city blocks. Central statistics offices can usually provide lists of EAs by “section” and large-scale maps of EAs selected for sampling (See @fig-sample25 and @fig-sample26). These maps make it easy to locate EAs and their boundaries. The sample of EAs can be decided using list-based or map-based (CSAS) sampling. In these settings, eligible persons may be sampled by moving from door-to-door. All dwellings in the selected block are sampled and all eligible persons in the selected dwellings are sampled. This means that all eligible persons in a selected block are sampled. -If city blocks are large then a type of systematic sampling may be used. With this method a rough map of the streets in the block is made and the number of doorways on each street is counted and copied onto the rough street map (as shown in Figure \@ref(fig:sample27)). The total number of doorways on all streets is calculated. A step size is calculated by dividing the total number of doorways on all streets by the number of dwellings to be sampled. A systematic sample along a route around the block that includes all streets in the block is taken. Streets can be sampled in any order. If you find that you have sampled all streets but have not yet sampled the required number of eligible persons then you should return to the street with the largest number of houses to collect the remainder of the sample. +If city blocks are large then a type of systematic sampling may be used. With this method a rough map of the streets in the block is made and the number of doorways on each street is counted and copied onto the rough street map (as shown in @fig-sample27). The total number of doorways on all streets is calculated. A step size is calculated by dividing the total number of doorways on all streets by the number of dwellings to be sampled. A systematic sample along a route around the block that includes all streets in the block is taken. Streets can be sampled in any order. If you find that you have sampled all streets but have not yet sampled the required number of eligible persons then you should return to the street with the largest number of houses to collect the remainder of the sample. The number of blocks to be sampled will depend on the expected number of eligible persons in each block. You should aim for an overall sample size of about $n = 192$. You should not sample fewer than $m = 16$ blocks. -```{r sample25, echo = FALSE, fig.cap = "Enumeration area map for a city block in Freetown, Sierra Leone", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample11.png") -``` +![Enumeration area map for a city block in Freetown, Sierra Leon](figures/stage2sample11.png){#fig-sample25 fig-alt="Enumeration area map for a city block in Freetown, Sierra Leone" fig-align="center"} \newpage -```{r sample26, echo = FALSE, fig.cap = "Enumeration area map for a city block in Addis Ababa, Ethiopia", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample12.png") -``` +![Enumeration area map for a city block in Addis Ababa, Ethiopia](figures/stage2sample12.png){#fig-sample26 fig-alt="Enumeration area map for a city block in Addis Ababa, Ethiopia" fig-align="center"} -```{r sample27, echo = FALSE, fig.cap = "Systematic sampling in a city block", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample13.png") -``` +![Systematic sampling in a city block](figures/stage2sample13.png){#fig-sample27 fig-alt="Systematic sampling in a city block" fig-align="center"} When useful lists and maps are not available then satellite imagery available though free services such as Google Earth (http://earth.google.com) may be used. The quality (resolution) of the images available from these services is variable but is usually good enough to allow you to segment the town into small areas of approximately equal volume (approximately the same number of dwellings) in each: -```{r sample28, echo = FALSE, fig.cap = "Segmenting a town into smaller sampling areas", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample14.png") -``` +![Segmenting a town into smaller sampling areas](figures/stage2sample14.png){#fig-sample28 fig-alt="Segmenting a town into smaller sampling areas" fig-align="center"} When creating segments using maps or satellite images it is a good idea to use main roads, rivers, canals, railway lines, public parks, etc as boundaries. This simplifies the segmentation process and also simplifies fieldwork by making areas and their boundaries easier to locate and sample. The first stage sample can be list-based (such as where each area is numbered in a systematic north to south and east to west order and a systematic sample taken) or map-based (CSAS). -Larger scale “maps” of blocks to be sampled can also me made using satellite imagery (see Figure \@ref(fig:sample29)). +Larger scale “maps” of blocks to be sampled can also me made using satellite imagery (see @fig-sample29). \newpage -```{r sample29, echo = FALSE, fig.cap = "A large scale “map” of a city block made from satellite imagery", fig.align = "center", fig.pos = "H", fig.retina = 1} -knitr::include_graphics("figures/stage2sample15.png") -``` +![A large scale map of a city block made from satellite imagery](figures/stage2sample15.png){#fig-sample29 fig-alt="A large scale map of a city block made from satellite imagery" fig-align="center"} \ No newline at end of file diff --git a/03-indicators.Rmd b/03-indicators.qmd similarity index 100% rename from 03-indicators.Rmd rename to 03-indicators.qmd diff --git a/04-questionnaire.Rmd b/04-questionnaire.qmd similarity index 100% rename from 04-questionnaire.Rmd rename to 04-questionnaire.qmd diff --git a/05-datasets.Rmd b/05-datasets.qmd similarity index 100% rename from 05-datasets.Rmd rename to 05-datasets.qmd diff --git a/06-practical.Rmd b/06-practical.qmd similarity index 100% rename from 06-practical.Rmd rename to 06-practical.qmd diff --git a/07-ram_op_software.Rmd b/07-ram_op_software.qmd similarity index 100% rename from 07-ram_op_software.Rmd rename to 07-ram_op_software.qmd diff --git a/08-conclusion.Rmd b/08-conclusion.qmd similarity index 100% rename from 08-conclusion.Rmd rename to 08-conclusion.qmd diff --git a/09-references.qmd b/09-references.qmd new file mode 100644 index 0000000..949fcca --- /dev/null +++ b/09-references.qmd @@ -0,0 +1 @@ +# References \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION deleted file mode 100644 index 06ac294..0000000 --- a/DESCRIPTION +++ /dev/null @@ -1,21 +0,0 @@ -Package: ramOPmanual -Title: Rapid Assessment Method for Older People (RAM-OP): The Manual -Version: 0.1.1 -Authors@R: c( - person("Pascale", "Fritsch", role = "aut"), - person("Ernest", "Guevarra", email = "ernest@guevarra.io", role = c("aut", "cre")), - person("Katja", "Siling", role = "aut"), - person("Mark", "Myatt", email = "mark@brixtonhealth.com", role = "aut")) -Description: HelpAge International , and Brixton Health , - with financial assistance from the Humanitarian Innovation Fund (HIF) - , have developed a Rapid Assessment Method for - Older People (RAM-OP) that provides accurate and reliable estimates of the - needs of older people. The method uses simple procedures, in a short time - frame (i.e. about two weeks including training, data collection, data entry, - and data analysis), and at considerably lower cost than other methods. -License: GPL-3 -Depends: R (>= 3.0.1) -Imports: - bookdown - diff --git a/README.md b/README.md index bd00790..da7d46c 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,4 @@ -# Rapid Assessment Method for Older People (RAM-OP): The Manual - -[![Travis-CI Build Status](https://travis-ci.org/rapidsurveys/ramOPmanual.svg?branch=master)](https://travis-ci.org/rapidsurveys/ramOPmanual) -[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/rapidsurveys/ramOPmanual?branch=master&svg=true)](https://ci.appveyor.com/project/rapidsurveys/ramOPmanual) -[![DOI](https://zenodo.org/badge/125880132.svg)](https://zenodo.org/badge/latestdoi/125880132) +# Rapid Assessment Method for Older People (RAM-OP): The Manual [HelpAge International](http://www.helpage.org), [VALID International](http://www.validinternational.org), and [Brixton Health](http://www.brixtonhealth.com), with financial assistance from the [Humanitarian Innovation Fund (HIF)](http://www.elrha.org/hif/home/), have developed a **Rapid Assessment Method for Older People (RAM-OP)** that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The **RAM-OP** method is based on the following principles: diff --git a/_bookdown.yml b/_bookdown.yml deleted file mode 100644 index 0fab082..0000000 --- a/_bookdown.yml +++ /dev/null @@ -1,10 +0,0 @@ -book_filename: "ramOPmanual" -repo: https://github.com/rapidsurveys/ramOPmanual -output_dir: "docs" -delete_merged_file: true -language: - label: - fig: "Figure " - tab: "Table " - ui: - chapter_name: "" diff --git a/_output.yml b/_output.yml deleted file mode 100644 index 92700d7..0000000 --- a/_output.yml +++ /dev/null @@ -1,31 +0,0 @@ -bookdown::gitbook: - highlight: tango - css: style.css - fig_caption: yes - split_by: chapter - #includes: - # in_header: google_analytics.html - config: - toc: - collapse: section - before: | -
  • RAM-OP
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  • - download: ["pdf", "epub"] - sharing: - github: yes - facebook: no - twitter: no -bookdown::pdf_book: - includes: - in_header: preamble.tex - latex_engine: xelatex - citation_package: natbib - keep_tex: yes - fig_caption: yes - toc_depth: 3 - toc_unnumbered: no - toc_appendix: yes -bookdown::epub_book: - stylesheet: style.css \ No newline at end of file diff --git a/_quarto.yml b/_quarto.yml new file mode 100644 index 0000000..abdba1f --- /dev/null +++ b/_quarto.yml @@ -0,0 +1,46 @@ +project: + type: book + output-dir: _site + +book: + title: Rapid Assessment Method for Older People (RAM-OP) + subtitle: The Manual + author: + - Pascale Fritsch + - Ernest Guevarra + - Katja Siling + - Mark Myatt + date: last-modified + date-format: "DD MMMM YYYY" + chapters: + - index.qmd + - 02-sampling.qmd + - 03-indicators.qmd + - 04-questionnaire.qmd + - 05-datasets.qmd + - 06-practical.qmd + - 07-ram_op_software.qmd + - 08-conclusion.qmd + - 09-references.qmd + cover-image: figures/coverImage.jpg + search: true + repo-url: https://github.com/rapidsurveys/ramOPmanual/ + repo-actions: [edit] + downloads: [pdf, epub] + #sharing: [mastodon, bluesky, linkedin] + google-analytics: + tracking-id: "G-SE438KE5DS" + anonymize-ip: true + +bibliography: references.bib + +format: + html: + theme: cosmo + highlight-style: breeze + # pdf: + # documentclass: scrreprt + # highlight-style: breeze + # epub: + # cover-image: figures/coverImage.jpg + # highlight-style: breeze \ No newline at end of file diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index c6c1438..0000000 --- a/appveyor.yml +++ /dev/null @@ -1,45 +0,0 @@ -# DO NOT CHANGE the "init" and "install" sections below - -# Download script file from GitHub -init: - ps: | - $ErrorActionPreference = "Stop" - Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1" - Import-Module '..\appveyor-tool.ps1' - -install: - ps: Bootstrap - -cache: - - C:\RLibrary - -# Adapt as necessary starting from here - -build_script: - - travis-tool.sh install_deps - -test_script: - - travis-tool.sh run_tests - -on_failure: - - 7z a failure.zip *.Rcheck\* - - appveyor PushArtifact failure.zip - -artifacts: - - path: '*.Rcheck\**\*.log' - name: Logs - - - path: '*.Rcheck\**\*.out' - name: Logs - - - path: '*.Rcheck\**\*.fail' - name: Logs - - - path: '*.Rcheck\**\*.Rout' - name: Logs - - - path: '\*_*.tar.gz' - name: Bits - - - path: '\*_*.zip' - name: Bits diff --git a/before_body.tex b/before_body.tex deleted file mode 100644 index dc41495..0000000 --- a/before_body.tex +++ /dev/null @@ -1 +0,0 @@ -\newpage \ No newline at end of file diff --git a/docs/conclusion.html b/docs/conclusion.html deleted file mode 100644 index d4defba..0000000 --- a/docs/conclusion.html +++ /dev/null @@ -1,293 +0,0 @@ - - - - - - - 7 Conclusion | Rapid Assessment Method for Older People (RAM-OP): The Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    7 Conclusion

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    We live in an ageing world, where people aged 60 or over will be 2 billion or about 22% of the world’s population by 2050.

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    Currently, two in three people aged 60 years or older live in developing countries. By 2050, nearly four in five older people will be living in the developing world.

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    The changing demographics of ageing combined with the increasing number of disasters will exert a disproportionate impact on the world’s oldest and poorest.

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    In this context, identifying the needs of older people as accurately as possible is a necessity. More and more donors and UN agencies are now willing to include older people in their programmes. Age markers, to complement gender markers, will be disseminated very soon

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    RAM-OP is offering a fast, robust, reliable, tested and user-friendly way of assessing the needs of older people. It can be used in humanitarian situations as well as in development contexts. The modular structure of RAM-OP allows for adaptations, making it exhaustive or limited to essential indicators according to the immediate needs.

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    As more organisations start to use it, RAM-OP will evolve and improve. New versions of RAM-OP can be created (for example, RAM-OP for refugee or displaced people camps). We wish that a greater number of actors will start using RAM-OP and make it their own.

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    4 Datasets

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    This section details the RAM-OP datasets. The information presented here is of most use if you decide not to use the RAM-OP data entry and data checking software. You might, for example, decide to enter survey data using spreadsheet software such as Microsoft Excel. If you do this and want to use the RAM-OP data analysis software then you will need to export the data as a comma-separated-value (CSV) file with the same variable names, variable types and lengths, and using the same codes as shown in the tables in this section. For the main RAM-OP survey dataset these are the same variable names, variable types, variable lengths, codes, and in the same order as shown on the standard RAM-OP questionnaire.

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    There are two RAM-OP datasets:

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    1. The main RAM-OP survey dataset : -This is the data collected by the survey questionnaire. The dataset definition for the main RAM-OP dataset is shown in Figure 4.1.

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    2. The PSU dataset : -This a short and narrow file with one record per PSU and just two variables:

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    psuThe PSU identifier. This must use the same coding system used to identify -PSUs that is used in the main RAM-OP dataset.
    popThe population of the PSU.
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    The PSU dataset is used during data-analysis to weight data by PSU population.

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    If you do not know population sizes (as might be the case in emergencies) then you can collect this data:

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    • When you visit the PSU (i.e. from community leaders or health centres).

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    • When you visit the PSU as a doorway count or roof count.

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    • Using recent satellite imagery as a roof count.

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    Relative population sizes can be used. If no better data is available then it is reasonable to use a simple semi-quantitative assessment such as:

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    Type of placePopulation range*FeaturesRecord population -as …
    Hamlet\(<\) 1,000Very small local market or no market1
    Village1,000 – 4,000Market and small shops serving the -village and the surrounding hamlets2
    Town\(>\) 4,000Large market, many shops (some -specialised), guest houses, bus -station, government offices4
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    *These ranges may need to be adjusted to match local circumstances.

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    The PSU dataset must be in comma-separated-value (CSV) format (see Figure 4.2) for use with the RAM-OP data analysis software.

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    -Main RAM-OP dataset definition -

    -Figure 4.1: Main RAM-OP dataset definition -

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    The RAM-OP data analysis requires that the main RAM-OP survey dataset is supplied in either an EpiInfo v6.xx or EpiData (REC) format or in a comma-separated-value (CSV) format file The RAM-OP data analysis requires that the PSU dataset is supplied in a comma-separated-value (.CSV) format file. Figure 4.2 shows an example of a PSU dataset in comma-separate-value (CSV) format.

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    -An example comma-separated-value (CSV) format file (the example is for a RAM-OP PSU dataset) -

    -Figure 4.2: An example comma-separated-value (CSV) format file (the example is for a RAM-OP PSU dataset) -

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    Note that the first line of a CSV format file gives the names of the variables (e.g. these are psu and pop for the PSU dataset) separated by commas. Subsequent lines contain data with items separated by commas and with one record per line. CSV format files can be created using a plain text editor (e.g. Notepad) or with a spreadsheet application such as Microsoft Excel™. If you use a spreadsheet application then you will have to be careful:

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    • Numbers with decimal places must use the full-stop character as the decimal separator. In some settings a spreadsheet application may want to use the comma character as the decimal separator.

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    • Avoid using accented characters in the names of and in the data entered into text variables. These characters can sometimes confuse the RAM-OP data analysis software. A CSV file should contain only plain text, number, and commas without formatting. Do not use a word processor application such as Microsoft Word™ to create or edit a CSV file.

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    If you have problems using a CSV file then you should check and edit the file using a plain text-editor such as Notepad or a dedicated CSV editor such as Ron’s Editor (http://www.ronsplace.eu/Products/RonsEditor)

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    The RAM-OP Manual

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    2 Indicators

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    2.1 The RAM-OP indicator set

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    RAM-OP surveys collect and report on data for a broad range of indicators relevant to older people.

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    These indicators cover the following dimensions:

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    • Demography and situation
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    • Food intake
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    • Severe food insecurity
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    • Disability
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    • Activities of daily living
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    • Mental health and well-being
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    • Dementia
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    • Health and health-seeking behaviour
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    • Sources of income
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    • Water, sanitation, and hygiene
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    • Anthropometry and screening coverage
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    • Visual impairment
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    Data for a small group of miscellaneous indicators are also collected and reported.

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    The RAM-OP indicator set has been designed on a modular basis. Each module is a set of indicators relating to a single dimension from the list given above and is collected using a dedicated set of questions and measurements. This means that the RAM-OP questionnaire also consists of a set of modules.

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    Whenever possible, RAM-OP uses standard and validated indicators and question sets.

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    Indicators are described below, showing the questionnaire components that are used to collect and record the data required, and flowcharts of the process used to derive indicators from the collected data. Standard symbols are used. For example:

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    A non-standard symbol is used to show recode operations. A recode operation shows changes that are made to data so that it can be used to derive indicators without having to show many decision nodes in the flowchart. They are also used to specify what should be done with missing or out-of-range values. For example:

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    2.1.1 Demography and situation

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    The demography and situation indicators are used to describe the survey sample and are derived from this questionnaire component:

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    Each of the questions yields a separate indicator:

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    2.1.2 Food intake

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    Food-intake indicators are derived from this questionnaire component. This data can be queried to yield a large number of useful indicators.

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    There are three related sets of diet-related indicators:

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    • meal frequency
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    • food groups consumed / dietary diversity
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    • indicators of nutrient consumption.
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    The indicator hierarchy is:

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    The data on the number of meals taken in the previous twenty-four hours forms a meal frequency score.

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    Food intake data from each subject is combined into a dietary diversity score. The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (i.e. no food groups) and eleven (i.e. eleven food groups). Higher values of the dietary diversity sore are associated with better food security.

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    The meal frequency score and the dietary diversity score follow:

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    • Swindale A, Bilinsky P, Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide.,Washington DC, Food and Nutrition Technical Assistance (FANTA) Project, 2006

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    • Kennedy G, Ballard T, Dop MC, Guidelines for Measuring Household and Individual Dietary Diversity, Rome, Food and Agricultural Organization, 2010

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    The data on the types of food consumed in the previous twenty-four hours are analysed in order to determine the diet’s content of specific micronutrients that are important for older people. This also follows Swindale & Bilinsky (2006) and Kennedy et al (2010), and:

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    • World Health Organisation, The management of nutrition in major emergencies, Geneva, WHO, 2000
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    2.1.3 Meal frequency

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    The meal frequency score indicator is the answer given to the first food intake question:

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    Meal frequency is a crude measure of food security.

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    Higher values of meal frequency are associated with better food security.

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    2.1.4 Food groups and dietary diversity

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    Questions relating to the consumption of individual food items / food types are combined to create food groups and the number of food groups consumed are counted to create a dietary diversity score:

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    The consumption of the eleven individual food groups and the dietary diversity score are reported separately.

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    The dietary diversity score is a crude measure of food security. The dietary diversity score ranges between zero (no food groups) and eleven (eleven food groups). Higher values of the dietary diversity score are associated with better food security.

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    2.1.5 Indicators of nutrient consumption

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    Overview

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    Questions and combinations of questions relating to the consumption of individual food items and food types can be used to determine whether the reported diet is likely to be provide sufficient nutrients of various types:

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    Each indicator is formed using logical “or” operations (i.e. the indicator is true if any of the constituent foods are consumed). For example, the indicator for the consumption of iron rich foods:

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    requires the consumption of one or more of green leafy vegetables, organ meats, meat, or fish and shellfish. Consumption of any of these foods is sufficient to indicate that the survey subject consumes iron rich food.

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    2.1.5.1 Protein rich foods

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    Indicators of consumption of protein rich foods from animal sources, plant source, and any / all sources are calculated as:

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    2.1.5.2 Vitamin A rich foods

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    Indicators of consumption of vitamin A rich foods from animal sources, plant source, and any / all sources are calculated as:

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    2.1.5.3 Iron rich foods

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    An indicator of consumption of iron rich foods from any / all sources is calculated as:

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    2.1.5.4 Calcium rich foods

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    An indicator of consumption of calcium rich foods from any / all sources is calculated as:

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    2.1.5.5 Zinc rich foods

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    An indicator of consumption of zinc rich foods from any / all sources is calculated as:

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    2.1.5.6 Vitamin B rich foods

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    Indicators of consumption of vitamin B rich foods from any / all sources are calculated as:

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    Note that the vitamin B complex indicator requires that at least one food from each of the B1, B2, B3, B6, and B12 rich food combinations is consumed.

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    2.1.6 Severe food insecurity

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    An indicator of severe food insecurity (hunger) is derived from this questionnaire component:

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    and is calculated as:

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    This indicator is the Household Hunger Scale (HHS) and is a simple, well-validated, and widely used indicator of severe food insecurity:

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    • Ballard T, Coates J, Swindale A, Deitchler M, Household Hunger Scale: Indicator Definition and Measurement Guide, Washington DC, FANTA-2 Bridge, FHI 360, 2011

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    • Ruel MT, Ballard TJ, Deitchler M, Measuring and Tracking the Access Dimension of Food Security: Available Indicators and Recommendations for Future Investments, Global Nutrition Report 2014: Technical Note 6, Washington DC, International Food Policy Research Institute, 2014

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    2.1.7 Disability

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    Indicators of disability across six different domains are derived from this questionnaire component:

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    Individual disability indicators are reported for each domain (i.e. vision, hearing, mobility, remembering, self-care, and communication) of disability in the Washington Group’s short set of question designed to identify people with a disability in a census or survey format:

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    Overall disability prevalence indicators are also reported.

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    Indicators of disability in each domain are calculated as:

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    Overall disability prevalence indicators are calculated as:

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    P0 = 1if no domain has D1 = 1, else = 0 (no disability in any domain)
    P1 = 1if at least one domain has D1 = 1, else = 0
    P2 = 1if at least one domain has D2 = 1, else = 0
    P3 = 1if at least one domain has D3 = 1, else = 0
    PM = 1if at more than one domain has D1 = 1, else = 0 (M stands for “Multiple”)
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    2.1.8 Activities of daily living

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    Indicators of how well the subject copes with activities of daily living are derived from this questionnaire component:

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    Individual independence indicators are reported for each dimension (i.e. bathing, dressing, toilet, mobility, continence, and eating) of daily living activities.

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    A composite indicator of the degree of independence (i.e. how well the subject can cope with activities of daily living) is also reported. This indicator is the Katz Index of Independence in Activities of Daily Living (or the Katz Index of ADL for short) and is a simple, well-validated, and widely used indicator of how well the subject can cope with activities of daily living:

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    • Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW, Studies of illness in the aged. The Index of ADL: A standardized measure of biological and psychosocial function, JAMA, 185(12), 1963, pp. 914-9

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    • Katz S, Down TD, Cash HR, Grotz, RC, Progress in the development of the index of ADL, The Gerontologist, 10(1), 1970, pp. 20-30

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    • Katz S, Assessing self-maintenance: Activities of daily living, mobility and instrumental activities of daily living, JAGS, 31(12), 1983, pp. 721-726

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    The Katz Index of ADL ranges between zero (complete dependence) and six (independence).

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    The seventh question of this module, which is not part of the Katz Index of ADL, is reported separately and indicates whether the subject has someone to help them with activities of daily living:

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    Activities of Daily Living
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    a7Is someone taking care of you or helping you with -everyday activities such as shopping, cooking, -bathing and dressing?1 = Yes; 2 = No[__]
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    It is not possible to know if the help available completely meets a subject’s needs, but we can identify the proportion of subjects needing help with one or more activities of daily living who also report not having someone to help them:

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    This is an indicator of unmet need.

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    Indicators of how well the subject can cope with activities of daily living and probable unmet need are calculated as:

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    2.1.9 Mental health and well-being

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    Indicators of mental health and well being are derived from this questionnaire component:

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    A score is calculated. This is the Kessler K6 Psychological Distress Scale. The score ranges from zero (indicating no psychological distress) to twenty-four (indicating severe psychological distress). A score of thirteen or more indicates serious psychological distress. The Kessler K6 Psychological Distress Scale is a widely recommended, widely used, accurate, reliable, and simple measure of psychological distress:

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    • Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT, et al, “Short screening scales to monitor population prevalences and trends in non-specific psychological distress”, Psychological Medicine, 32(6), 2002, pp. 959–976

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    • Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, “Screening for Serious Mental Illness in the General Population”, Archives of General Psychiatry, 60(2), 2003, pp. 184-189

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    Indicators of mental health and well-being are calculated as:

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    2.1.10 Dementia

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    An indicator of probable dementia is derived from this questionnaire component:

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    The indicator of probable dementia is calculated as:

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    This indicator is derived from the Community Screening Instrument for Dementia (CSID) developed by the 10/66 Dementia Research Group. This is a simple, validated, and widely used indicator of probable dementia:

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    • Prince M, et al, “A brief dementia screener suitable for use by non-specialists in resource poor settings - The cross-cultural derivation and validation of the brief Community Screening Instrument for Dementia”, International Journal of Geriatric Psychiatry, 26(9), 2011, pp. 899–907
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    2.1.11 Health and health-seeking behaviour

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    Indicators of health and health-seeking behaviour for chronic and acute conditions are derived from this questionnaire component:

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    Indicators of health and health-seeking behaviour for chronic conditions are calculated as:

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    Indicators of health and health-seeking behaviour for acute conditions are calculated as:

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    2.1.12 Sources of income

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    Indicators related to sources of income are derived from this questionnaire component:

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    and are calculated as:

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    The grouped income sources (i.e. m2a, m2b, etc.) and individual income sources may vary between settings. The questionnaire component shown above has proved suitable for use in Ethiopia, South Sudan, and Tanzania.

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    2.1.13 Water, sanitation, and hygiene

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    Indicators relating to water, sanitation, and hygiene (WASH) are derived from this questionnaire component:

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    Indicators are calculated following:

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    Indicators relating to water, sanitation, and hygiene (WASH) are calculated as:

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    2.1.14 Anthropometry and screening coverage

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    Indicators relating to anthropometry and screening coverage are derived from this questionnaire component:

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    -And are calculated as:

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    Raw MUAC data (i.e. not MUAC class) is collected, entered, and analysed. This requires that an adult MUAC tape (i.e. capable of measuring MUAC to 450 mm) is used.

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    The presence of bilateral oedema is assessed by pressing with your thumbs both feet of the older person for three seconds and checking whether this creates a lasting depression or “pit” on both feet. Bilateral pitting oedema in older people may not be “nutritional” oedema (as is almost always the case with children). Older people with bilateral pitting oedema should be advised to consult a doctor.

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    The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes:

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    • World Health Organisation, Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854, WHO, Geneva, 1995

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    • Dale NM, Myatt M, Prudhon C, Briend, A, “Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys”, Public Health Nutrition, 1–6. doi:10.1017/S1368980012003345, 2012

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    • Blanton CJ, Bilukha, OO, “The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision”, Emerging Themes in Epidemiology, 10(1), 2013, p. 8

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    The PROBIT estimator is described in Box 1.

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    MUAC-based case definitions for acute malnutrition are used:

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    GAM
    MUAC < 210 mm
    MAM
    185 mm ≤ MUAC < 210mm
    SAM
    MUAC < 185mm
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    These are standard case definitions for acute malnutrition in adults and recommended by HelpAge International for use in older people in humanitarian contexts.

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    Note : MUAC in adults should be measured on the non-dominant arm. This is usually the left arm. The importance of high levels of accuracy and precision at the individual level is of lesser importance in survey work compared to case-finding or diagnosis in clinical contexts, for example. This means that a simple rule such as “Always measure MUAC on the left arm” may be used.

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    An estimate of GAM prevalence can be made using a classic estimator:

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    \[\text{prevalence} = \frac{\text{number of respondents with MUAC < 210 mm}}{\text{total number of respondents}}\]

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    The estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the inverse cumulative distribution function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a normally distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are predicted (or estimated) to fall below the 210 mm threshold.

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    Both the classic and the PROBIT methods can be thought of as estimating area:

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    The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method.

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    The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality.

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    The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates:

    -\[\widehat{MAM prevalence} = \widehat{GAM prevalence} - \widehat{SAM prevalence}\] -
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    2.1.15 Visual impairment

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    An indicator of visual impairment is derived from this questionnaire component:

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    And is calculated as:

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    The “illiterate E” or “tumbling E” (the preferred term) is a validated and widely used method for measuring visual acuity:

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    • Taylor HR, “Applying new design principles to the construction of an illiterate E chart”, American Journal of Optometry & Physiological Optics, 55:348, 1978

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    • Kaiser PK, “Prospective Evaluation of Visual Acuity Assessment: A Comparison of Snellen Versus ETDRS Charts in Clinical Practice (An AOS Thesis)”, Transactions of the American Ophthalmological Society, 107: 311–324, 2009

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    The size of the “E” used:

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    as well as the distance used for the test (two metres) and the indicator calculation apply the WHO case definition of visual impairment (i.e. visual acuity < 6 / 18).

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    The tumbling E card should be laminated (i.e. plastic coated and have a two metre cord attached which helps to ensure that the visual acuity test is performed at the correct distance (See Figure 2.1).

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    After demonstrating to the respondent what the test is about (i.e. the subject should indicate which direction the branches of the ‘E’ are pointing), the test is administered at a distance of two meters, turning the card in four different directions, and asking the person to indicate which direction the branches of the “E” is pointing. If the subject wears glasses, they are allowed to use them during the test if they want to.

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    Note : If the person is unable to correctly answer at least three times out of four, they have a visual impairment. A simple visual acuity test such as the ‘tumbling E’ test also does not indicate anything about an underlying disease such as glaucoma or the need for reading spectacles (presbyopia). These conditions are common in people aged 60 years or older. Subjects failing the visual acuity test should be counselled to visit an ophthalmologist for a detailed eye examination.

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    -Equipment used to measure visual acuity -

    -Figure 2.1: Equipment used to measure visual acuity -

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    2.1.16 Miscellaneous indicators

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    Data for a small group of miscellaneous indicators are also collected and reported. These are derived from these questions:

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    Hunger – Ration - Relief

    - ------ - - - - - - - - - - - - - - -
    f6Are you or anyone in your household receiving a -food ration on a regular basis?1 = Yes; 2 = No[__]
    f7Have you or another member of your household -received non-food relief items such as soap, -bucket, water container, bedding, mosquito net, -clothes, or plastic sheet in the previous four -weeks?1 = Yes; 2 = No[__]
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    Activities of Daily Living

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    a8Do you have problems chewing food?1 = Yes; 2 = No[__]
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    and are calculated as:

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    2.2 A note on data management and data analysis

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    This section has described how RAM-OP data is used to create a broad set of indicators. If you do not want to use the standard RAM-OP software to do this then you can use this information to create data entry systems and data management scripts for your favoured database or statistical analysis software. See the sections on RAM-OP datasets and RAM-OP questionnaire for more compact information on variable names and codes that you may find helpful.

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    It is important to note that data analysis procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note:

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    • The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs).
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    • The RAM-OP sample is not prior weighted. This means that you will need to provide per-PSU sampling weights. These are usually the populations of the PSU.
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    You will need to specify this sample design to your statistical analysis software. If you fail to do this then your analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e. they will be too narrow).

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    The standard RAM-OP software uses blocked weighted bootstrap estimation approach:

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    • Blocked : The block corresponds to the PSU or cluster.
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    • Weighted : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a “roulette wheel” algorithm to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates.
      • -
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    A total of m' PSUs are sampled with-replacement from the survey dataset where m' is the number of PSUs in the survey sample. Individual records within each PSU are then sampled with-replacement. A total of n’ records are sampled with-replacement from each of the selected PSUs where n' is the number of individual records in a selected PSU. The resulting collection of records replicates the original survey in terms of both sample design and sample size. A large number of replicate surveys are taken (the standard RAM-OP software uses \(r = 399\) replicate surveys but this can be changed). The required statistic (e.g. the mean of an indicator value) is applied to each replicate survey. The reported estimate consists of the 50th (point estimate), 2.5th (lower 95% confidence limit), and the 97.5th (upper 95% confidence limit) percentiles of the distribution of the statistic observed across all replicate surveys. The blocked weighted bootstrap procedure is outlined in Figure 2.2.

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    The principal advantages of using a bootstrap estimator are:

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    • The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods.
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    The standard RAM-OP data analysis software is described in the section Standard RAM-OP software.

    - -
    -The blocked weighted bootstrap used by the standard RAM-OP software -

    -Figure 2.2: The blocked weighted bootstrap used by the standard RAM-OP software -

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    - - - - - - - - - - - - - - diff --git a/docs/introduction.html b/docs/introduction.html deleted file mode 100644 index 302b5b7..0000000 --- a/docs/introduction.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - Introduction | Rapid Assessment Method for Older People (RAM-OP): The Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - -
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    Introduction

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    Older people (generally defined as people aged sixty years and older) are a vulnerable group for malnutrition in humanitarian and developmental contexts. Due to their age they have specific nutritional needs, such as easily digestible and palatable food adapted to those with chewing problems, which is dense in nutrients. In famine and displacement situations where populations are dependent on food distributions, older people often find the general ration inappropriate to their tastes and needs, have difficulties accessing the distributions, or have difficulties transporting rations home. As a result, older people can become malnourished and in need of specifically targeted food interventions. In times of drought or food scarcity, older people tend to reduce their food intake in order to share or give up their ration to younger members of their families. They are then at risk of malnutrition.

    -

    Despite these potential vulnerabilities in humanitarian situations, older people are rarely identified as a group in need of specific nutritional or food assistance. Surveys and assessments almost always focus on children, and sometimes on pregnant and lactating women. Humanitarian workers argue that assessing the nutritional status and needs of older people is both costly and complicated. As a consequence, the nutritional status and needs of older people in crisis go unidentified and unaddressed.

    -

    HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles:

    -
      -

    • Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample (\(m ≥ 16\) clusters) and a small overall (\(n ≥ 192\)) sample.

      • -
    • Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets.
      • -

    • Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size.

      • -
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    The following tools are currently available under the General Public Licence / Free Documentation License, meaning that you are free to copy and adapt these tools:

    -
      -

    • an English language manual / guidebook

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    • a questionnaire (available in English and French)

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    • data entry and data checking software (available in English and French)

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    • data analysis software.

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    We believe that the availability of a rapid, low-cost, and user-friendly method will encourage governments, UN agencies, as well as international and local non-governmental organisations to actively assess the situation of older people in humanitarian contexts, and implement, monitor, and evaluate relevant and timely responses to address their needs.

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isObj=isObject(source),props=isObj?keys(source):undefined,methodNames=props&&props.length?baseFunctions(source,props):undefined;if(!(methodNames?methodNames.length:isObj)){methodNames=false;options=source;source=object;object=this}}if(!methodNames){methodNames=baseFunctions(source,keys(source))}var chain=true,index=-1,isFunc=isFunction(object),length=methodNames.length;if(options===false){chain=false}else if(isObject(options)&&"chain"in options){chain=options.chain}while(++index0||end<0)){return new LazyWrapper(result)}if(start<0){result=result.takeRight(-start)}else if(start){result=result.drop(start)}if(end!==undefined){end=+end||0;result=end<0?result.dropRight(-end):result.take(end-start)}return result};LazyWrapper.prototype.takeRightWhile=function(predicate,thisArg){return this.reverse().takeWhile(predicate,thisArg).reverse()};LazyWrapper.prototype.toArray=function(){return this.take(POSITIVE_INFINITY)};baseForOwn(LazyWrapper.prototype,function(func,methodName){var 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if(freeExports&&freeModule){if(moduleExports){(freeModule.exports=_)._=_}else{freeExports._=_}}else{root._=_}}).call(this)}).call(this,typeof global!=="undefined"?global:typeof self!=="undefined"?self:typeof window!=="undefined"?window:{})},{}],3:[function(require,module,exports){(function(window,document,undefined){var _MAP={8:"backspace",9:"tab",13:"enter",16:"shift",17:"ctrl",18:"alt",20:"capslock",27:"esc",32:"space",33:"pageup",34:"pagedown",35:"end",36:"home",37:"left",38:"up",39:"right",40:"down",45:"ins",46:"del",91:"meta",93:"meta",224:"meta"};var _KEYCODE_MAP={106:"*",107:"+",109:"-",110:".",111:"/",186:";",187:"=",188:",",189:"-",190:".",191:"/",192:"`",219:"[",220:"\\",221:"]",222:"'"};var _SHIFT_MAP={"~":"`","!":"1","@":"2","#":"3",$:"4","%":"5","^":"6","&":"7","*":"8","(":"9",")":"0",_:"-","+":"=",":":";",'"':"'","<":",",">":".","?":"/","|":"\\"};var _SPECIAL_ALIASES={option:"alt",command:"meta",return:"enter",escape:"esc",plus:"+",mod:/Mac|iPod|iPhone|iPad/.test(navigator.platform)?"meta":"ctrl"};var _REVERSE_MAP;for(var i=1;i<20;++i){_MAP[111+i]="f"+i}for(i=0;i<=9;++i){_MAP[i+96]=i}function _addEvent(object,type,callback){if(object.addEventListener){object.addEventListener(type,callback,false);return}object.attachEvent("on"+type,callback)}function _characterFromEvent(e){if(e.type=="keypress"){var character=String.fromCharCode(e.which);if(!e.shiftKey){character=character.toLowerCase()}return character}if(_MAP[e.which]){return _MAP[e.which]}if(_KEYCODE_MAP[e.which]){return _KEYCODE_MAP[e.which]}return String.fromCharCode(e.which).toLowerCase()}function _modifiersMatch(modifiers1,modifiers2){return modifiers1.sort().join(",")===modifiers2.sort().join(",")}function _eventModifiers(e){var modifiers=[];if(e.shiftKey){modifiers.push("shift")}if(e.altKey){modifiers.push("alt")}if(e.ctrlKey){modifiers.push("ctrl")}if(e.metaKey){modifiers.push("meta")}return modifiers}function _preventDefault(e){if(e.preventDefault){e.preventDefault();return}e.returnValue=false}function _stopPropagation(e){if(e.stopPropagation){e.stopPropagation();return}e.cancelBubble=true}function _isModifier(key){return key=="shift"||key=="ctrl"||key=="alt"||key=="meta"}function _getReverseMap(){if(!_REVERSE_MAP){_REVERSE_MAP={};for(var key in _MAP){if(key>95&&key<112){continue}if(_MAP.hasOwnProperty(key)){_REVERSE_MAP[_MAP[key]]=key}}}return _REVERSE_MAP}function _pickBestAction(key,modifiers,action){if(!action){action=_getReverseMap()[key]?"keydown":"keypress"}if(action=="keypress"&&modifiers.length){action="keydown"}return action}function _keysFromString(combination){if(combination==="+"){return["+"]}combination=combination.replace(/\+{2}/g,"+plus");return combination.split("+")}function _getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i1){for(var i=1;i= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter=55296&&value<=56319&&counter65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digitfloor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j=n&¤tValuefloor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;jmaxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q0&&len>maxKeys){len=maxKeys}for(var i=0;i=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i",'"',"`"," ","\r","\n","\t"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}result.search=relative.search;result.query=relative.query;if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.href=result.format();return result}if(!srcPath.length){result.pathname=null;if(result.search){result.path="/"+result.search}else{result.path=null}result.href=result.format();return result}var last=srcPath.slice(-1)[0];var hasTrailingSlash=(result.host||relative.host)&&(last==="."||last==="..")||last==="";var up=0;for(var i=srcPath.length;i>=0;i--){last=srcPath[i];if(last=="."){srcPath.splice(i,1)}else if(last===".."){srcPath.splice(i,1);up++}else if(up){srcPath.splice(i,1);up--}}if(!mustEndAbs&&!removeAllDots){for(;up--;up){srcPath.unshift("..")}}if(mustEndAbs&&srcPath[0]!==""&&(!srcPath[0]||srcPath[0].charAt(0)!=="/")){srcPath.unshift("")}if(hasTrailingSlash&&srcPath.join("/").substr(-1)!=="/"){srcPath.push("")}var isAbsolute=srcPath[0]===""||srcPath[0]&&srcPath[0].charAt(0)==="/";if(psychotic){result.hostname=result.host=isAbsolute?"":srcPath.length?srcPath.shift():"";var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}mustEndAbs=mustEndAbs||result.host&&srcPath.length;if(mustEndAbs&&!isAbsolute){srcPath.unshift("")}if(!srcPath.length){result.pathname=null;result.path=null}else{result.pathname=srcPath.join("/")}if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.auth=relative.auth||result.auth;result.slashes=result.slashes||relative.slashes;result.href=result.format();return result};Url.prototype.parseHost=function(){var host=this.host;var port=portPattern.exec(host);if(port){port=port[0];if(port!==":"){this.port=port.substr(1)}host=host.substr(0,host.length-port.length)}if(host)this.hostname=host};function isString(arg){return typeof arg==="string"}function isObject(arg){return typeof arg==="object"&&arg!==null}function isNull(arg){return arg===null}function isNullOrUndefined(arg){return arg==null}},{punycode:6,querystring:9}],11:[function(require,module,exports){var $=require("jquery");function toggleDropdown(e){var $dropdown=$(e.currentTarget).parent().find(".dropdown-menu");$dropdown.toggleClass("open");e.stopPropagation();e.preventDefault()}function closeDropdown(e){$(".dropdown-menu").removeClass("open")}function init(){$(document).on("click",".toggle-dropdown",toggleDropdown);$(document).on("click",".dropdown-menu",function(e){e.stopPropagation()});$(document).on("click",closeDropdown)}module.exports={init:init}},{jquery:1}],12:[function(require,module,exports){var $=require("jquery");module.exports=$({})},{jquery:1}],13:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var dropdown=require("./dropdown");var events=require("./events");var state=require("./state");var keyboard=require("./keyboard");var navigation=require("./navigation");var sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",label:"Toggle Sidebar",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var navigation=require("./navigation");var sidebar=require("./sidebar");function bindShortcut(keys,fn){Mousetrap.bind(keys,function(e){fn();return false})}function init(){bindShortcut(["right"],function(e){navigation.goNext()});bindShortcut(["left"],function(e){navigation.goPrev()});bindShortcut(["s"],function(e){sidebar.toggle()})}module.exports={init:init,bind:bindShortcut}},{"./navigation":16,"./sidebar":18,mousetrap:3}],15:[function(require,module,exports){var state=require("./state");function showLoading(p){state.$book.addClass("is-loading");p.always(function(){state.$book.removeClass("is-loading")});return p}module.exports={show:showLoading}},{"./state":19}],16:[function(require,module,exports){var $=require("jquery");var url=require("url");var events=require("./events");var state=require("./state");var loading=require("./loading");var usePushState=typeof history.pushState!=="undefined";function handleNavigation(relativeUrl,push){var uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var url=$(this).attr("href");if(url)handleNavigation(url,true)}function goNext(){var url=$(".navigation-next").attr("href");if(url)handleNavigation(url,true)}function goPrev(){var url=$(".navigation-prev").attr("href");if(url)handleNavigation(url,true)}function init(){$.ajaxSetup({});if(location.protocol!=="file:"){history.replaceState({path:window.location.href},"")}window.onpopstate=function(event){if(event.state===null){return}return handleNavigation(event.state.path,false)};$(document).on("click",".navigation-prev",handlePagination);$(document).on("click",".navigation-next",handlePagination);$(document).on("click",".summary [data-path] a",handlePagination);$(window).resize(updateNavigationPosition);preparePage(false)}module.exports={init:init,goNext:goNext,goPrev:goPrev,notify:notifyPageChange}},{"./events":12,"./loading":15,"./state":19,jquery:1,url:10}],17:[function(require,module,exports){module.exports={isMobile:function(){return document.body.clientWidth<=600}}},{}],18:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var platform=require("./platform");var state=require("./state");function toggleSidebar(_state,animation){if(state!=null&&isOpen()==_state)return;if(animation==null)animation=true;state.$book.toggleClass("without-animation",!animation);state.$book.toggleClass("with-summary",_state);storage.set("sidebar",isOpen())}function isOpen(){return state.$book.hasClass("with-summary")}function init(){if(platform.isMobile()){toggleSidebar(false,false)}else{toggleSidebar(storage.get("sidebar",true),false)}$(document).on("click",".book-summary li.chapter a",function(e){if(platform.isMobile())toggleSidebar(false,false)})}function filterSummary(paths){var $summary=$(".book-summary");$summary.find("li").each(function(){var path=$(this).data("path");var st=paths==null||_.contains(paths,path);$(this).toggle(st);if(st)$(this).parents("li").show()})}module.exports={init:init,isOpen:isOpen,toggle:toggleSidebar,filter:filterSummary}},{"./platform":17,"./state":19,"./storage":20,jquery:1,lodash:2}],19:[function(require,module,exports){var $=require("jquery");var url=require("url");var path=require("path");var state={};state.update=function(dom){var $book=$(dom.find(".book"));state.$book=$book;state.level=$book.data("level");state.basePath=$book.data("basepath");state.innerLanguage=$book.data("innerlanguage");state.revision=$book.data("revision");state.filepath=$book.data("filepath");state.chapterTitle=$book.data("chapter-title");state.root=url.resolve(location.protocol+"//"+location.host,path.dirname(path.resolve(location.pathname.replace(/\/$/,"/index.html"),state.basePath))).replace(/\/?$/,"/");state.bookRoot=state.innerLanguage?url.resolve(state.root,".."):state.root};state.update($);module.exports=state},{jquery:1,path:4,url:10}],20:[function(require,module,exports){var baseKey="";module.exports={setBaseKey:function(key){baseKey=key},set:function(key,value){key=baseKey+":"+key;try{sessionStorage[key]=JSON.stringify(value)}catch(e){}},get:function(key,def){key=baseKey+":"+key;if(sessionStorage[key]===undefined)return def;try{var v=JSON.parse(sessionStorage[key]);return v==null?def:v}catch(err){return sessionStorage[key]||def}},remove:function(key){key=baseKey+":"+key;sessionStorage.removeItem(key)}}},{}],21:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var events=require("./events");var buttons=[];function insertAt(parent,selector,index,element){var lastIndex=parent.children(selector).size();if(index<0){index=Math.max(0,lastIndex+1+index)}parent.append(element);if(index",{class:"dropdown-menu",html:'
    '});if(_.isString(dropdown)){$menu.append(dropdown)}else{var groups=_.map(dropdown,function(group){if(_.isArray(group))return group;else return[group]});_.each(groups,function(group){var $group=$("
    ",{class:"buttons"});var sizeClass="size-"+group.length;_.each(group,function(btn){btn=_.defaults(btn||{},{text:"",className:"",onClick:defaultOnClick});var $btn=$("