prevR-package {prevR}

Estimating regional trends of a prevalence from a DHS.

Description

prevR allows spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey.

Details

Package:	prevR
Type:	Package
Licence:	CeCILL-C - https://cecill.info/licences/Licence_CeCILL-C_V1-en.html
Website:	https://larmarange.github.io/prevR/

This package performs a methodological approach for spatial estimation of regional trends of a prevalence using data from surveys using a stratified two-stage sample design (as Demographic and Health Surveys). In these kind of surveys, positive and control cases are spatially positioned at the centre of their corresponding surveyed cluster.

This package provides functions to estimate a prevalence surface using a kernel estimator with adaptative bandwidths of equal number of persons surveyed (a variant of the nearest neighbor technique) or with fixed bandwidths. The prevalence surface could also be calculated using a spatial interpolation (kriging or inverse distance weighting) after a moving average smoothing based on circles of equal number of observed persons or circles of equal radius.

With the kernel estimator approach, it's also possible to estimate a surface of relative risks.

For a quick demo, enter quick.prevR(fdhs).

For a full demo, enter demo(prevR).

The content of prevR can be broken up as follows:

Datasets
fdhs is a fictive dataset used for testing the package.
TMWorldBorders provides national borders of every countries in the World and could be used to define the limits of the studied area.

Creating objects
prevR functions takes as input objects of class prevR.
import.dhs() allows to import easily, through a step by step procedure, data from a DHS (Demographic and Health Surveys) downloaded from http://www.measuredhs.com.
as.prevR() is a generic function to create an object of class prevR.
create.boundary() could be used to select borders of a country and transfer them to as.prevR() in order to define the studied area.

Data visualization
Methods show(), print() and summary() display a summary of a object of class prevR.
The method plot() could be used on a object of class prevR for visualizing the studied area, spatial position of clusters, number of observations or number of positive cases by cluster.

Data manipulation
The method changeproj() changes the projection of the spatial coordinates.
The method as.data.frame() converts an object of class prevR into a data frame.
The method export() export data and/or the studied area in a text file, a dbf file or a shapefile.

Data analysis
rings() calculates rings of equal number of observations and/or equal radius.
kde() calculates a prevalence surface or a relative risks surface using gaussian kernel density estimators (kde) with adaptative bandwidths.
krige() executes a spatial interpolation using an ordinary kriging.
idw() executes a spatial interpolation using an inverse distance weighting (idw) technique.

Acknowledgment

prevR has been developed with funding from the French National Agency for Research on AIDS and Viral Hepatitis (ANRS - http://www.anrs.fr) and the French Research Institute for Sustainable Development (IRD - https://www.ird.fr), and technical support from LYSIS ([e-mail]).

Citation

To cite prevR:
Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe and Meda Nicolas (2011) "Methods for mapping regional trends of HIV prevalence from Demographic and Health Surveys (DHS)", Cybergeo: European Journal of Geography, no 558, https://journals.openedition.org/cybergeo/24606, DOI: 10.4000/cybergeo.24606.

Author(s)

Joseph Larmarange [e-mail]
IRD - CEPED (UMR 196 Université Paris Descartes Ined IRD)

References

Larmarange Joseph and Bendaud Victoria (2014) "HIV estimates at second subnational level from national population-based survey", AIDS, n° 28, p. S469-S476, DOI: 10.1097/QAD.0000000000000480

Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe and Meda Nicolas (2011) "Methods for mapping regional trends of HIV prevalence from Demographic and Health Surveys (DHS)", Cybergeo: European Journal of Geography, n° 558, https://journals.openedition.org/cybergeo/24606, DOI: 10.4000/cybergeo.24606

Larmarange Joseph (2007) Prévalences du VIH en Afrique : validité d'une mesure, PhD thesis in demography, directed by Benoît Ferry, université Paris Descartes, https://theses.hal.science/tel-00320283.

Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe Meda Nicolas and Ferry Benoît (2006), "Cartographier les données des enquêtes démographiques et de santé à partir des coordonnées des zones d'enquête", Chaire Quételet, 29 novembre au 1er décembre 2006, Université Catholique de Louvain, Louvain-la-Neuve, Belgique.

Examples

## Not run: 
par(ask = TRUE)
# Creating an object of class prevR
col <- c(
  id = "cluster",
  x = "x",
  y = "y",
  n = "n",
  pos = "pos",
  c.type = "residence",
  wn = "weighted.n",
  wpos = "weighted.pos"
)
dhs <- as.prevR(fdhs.clusters, col, fdhs.boundary)

str(dhs)
print(dhs)

plot(dhs, main = "Clusters position")
plot(dhs, type = "c.type", main = "Clusters by residence")
plot(dhs, type = "count", main = "Observations by cluster")
plot(dhs, type = "flower", main = "Positive cases by cluster")

# Changing coordinates projection
plot(dhs, axes = TRUE)
dhs <- changeproj(
  dhs,
  "+proj=utm +zone=30 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
)
print(dhs)
plot(dhs, axes = TRUE)

# Calculating rings of equal number of observations for different values of N
dhs <- rings(dhs, N = c(100, 200, 300, 400, 500))
print(dhs)
summary(dhs)

# Prevalence surface for N=300
prev.N300 <- kde(dhs, N = 300, nb.cells = 200)
plot(
  prev.N300["k.wprev.N300.RInf"],
  pal = prevR.colors.red,
  lty = 0,
  main = "Regional trends of prevalence (N=300)"
)

# Smoothing ring radii surface (spatial interpolation by kriging)
radius.N300 <- krige("r.radius", dhs, N = 300, nb.cells = 200)
plot(
  radius.N300,
  pal = prevR.colors.blue,
  lty = 0,
  main = "Radius of circle (N=300)"
)
par(ask = FALSE)

## End(Not run)