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Adam Howes edited this page Nov 1, 2021 · 17 revisions

Improving the estimates for FSW

  • Previous estimates from workbook are based upon national estimates of FSW population size. Think about how to integrate these
  • Biases and variation in methodology for key population data which vary by country. Survey estimates have more comparable methodology but depending on KP features (for example proportion in households included in survey sampling frame) may have varying bias. See working paper "Laga - Mapping the population size of female sex worker in countries across sub-Saharan Africa"Math Kernel Library
  • Survey question "did you have sex in exchange for money or goods" has been critisied. Likely too broad with regard to key population of female sex workers
    • Only the most recent round of DHS has this survey question (change was made in 2013 and started to be implemented in 2015, although this may vary by country with some countries still not using this question). Alternative question regards last three sexual partners
  • Other sources of data about key populations
    • The UNAIDS Key Population Atlas
    • Johnston et al. (2021, preprint) Deriving and interpreting population size estimates for adolescent and young key populations at higher risk of HIV transmission: men who have sex with men, female sex workers and transgender persons
      • Disaggregates the UNAIDS published population size estimates by age using proportion of sexually active adults
      • Kinh is a coauthor
        • Warns that the estimates should be seen as expert opinion rather than based on data
        • Several countries had no data
        • Rounding up when the number is too small
    • Laga et al. (2021, preprint) Mapping female sex worker prevalence (aged 15-49 years) in sub-Saharan Africa
      • Has code and data
      • Jeff is a coauthor
        • Variation across countries may be implausibly large
        • Uses study type random effects but implementation differences even within studies belonging to the same group likely to be large
  • Possible approaches
    • Move all sexpaid12m into sexnonreg, then get the FSW estimates from other data sources. Is there a way to integrate this data in a coherent way?
    • Use the sexpaid12m data to learn the spatial pattern and the other data sources to learn the level
  • Is there a coherent way to use existing estimates? Penalise distance from existing estimates equivalent to placing a prior on estimate?
    • Sounds similar to Bayesian melding, but implementing Bayesian melding is intractable for all but the simplest models
    • Can we get distributions or standard errors on the existing estimates? Not for Johnston
    • Work of Jon Wakefield / Taylor Okonek on calibration of estimates?
  • Other possible data source on men who paid for sex
    • See Hodgkins et al. (2021, preprint)
    • The proportion of men who pay for sex (CFSW) can be estimated from the data, and then this can be linked to the proportion of FSW by some model like p_{CFSW} = B * p_{FSW} where a strongly informative prior is placed on B (around 10 say)
  • Fully Bayesian benchmarking of small area estimation models (Zhang and Bryant, 2020)
    • Zhang and Bryant have quite a few papers which look interesting

Misc to-do

  • Analysis of the extent of the differences between the different models e.g. compute maximum difference between (mean) estimates then arrange in decreasing order
  • Possibility to include covariates
  • Extend Malawi model by adding more surveys (PHIA and MICS). Could use survey specific intercepts
  • Fitting model jointly to multiple countries
  • Should the utils scripts be reports? Report to run reports? Report to run report which runs reports?
    • When running an orderly report is it possible to create data outside of the draft folder?
    • run_fitting is probably best staying as external to orderly anyway
    • pull_naomi_areas might be better using orderly_pull_dependencies
  • Add other different types of simulated data e.g. spatial structure to sim_sexbehav and try to recover
  • Create individual data that links the cluster_id to area by modifying mwi_data_survey_behav (currently it's only the aggregate data that is output)
  • Fit model to Malawi using individual format data. Individual weighted log-likelihood in R-INLA might not be possible, see Google group discussion. Could try TMB

Resources

Notes

  • The 13 AGYW Global Fund priority countries are Botswana, Cameroon, Kenya, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe, from "The Global Fund measurement framework for adolescents girls and young women programs"
  • Use same model for all countries or select to best model in each country?
    • Same model for all is a good default, unless something really stands out
  • In which countries, ages or categories are there the greatest changes?
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