@article{fdi:010067180,
  title = {{E}valuation of geospatial methods to generate subnational {HIV} prevalence estimates for local level planning [consise communication]},
  author = {{L}armarange, {J}oseph},
  editor = {},
  language = {{ENG}},
  abstract = {{O}bjective: {T}here is evidence of substantial subnational variation in the {HIV} epidemic. {H}owever, robust spatial {HIV} data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. {T}herefore, spatial analysis methods that leverage available data to provide local estimates of {HIV} prevalence may be useful. {S}uch methods exist but have not been formally compared when applied to {HIV}. {D}esign/methods: {S}ix candidate methods – including those used by the {J}oint {U}nited {N}ations {P}rogramme on {HIV}/{AIDS} to generate maps and a {B}ayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of {HIV} prevalence across three countries using cluster level data from household surveys. {T}wo approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. {R}esults: {E}ach of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. {H}owever, the {B}ayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. {C}onclusions: {A}vailable methods may be able to furnish estimates of {HIV} prevalence at finer spatial scales than the data currently allow. {T}he subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. {T}he {B}ayesian geostatistical approach is a promising strategy for integrating {HIV} data to generate robust local estimates.},
  keywords = {{AFRIQUE} {SUBSAHARIENNE}},
  booktitle = {},
  journal = {{A}ids},
  volume = {30},
  numero = {9},
  pages = {1467--1474},
  ISSN = {0269-9370},
  year = {2016},
  DOI = {10.1097/{QAD}.0000000000001075},
  URL = {https://www.documentation.ird.fr/hor/fdi:010067180},
}