Imperial College London
Alternate

ProfessorSimonGregson

Faculty of Medicine, School of Public Health

Professor in Demography and Behavioural Science
 
 
 
//

Contact

 

+44 (0)20 7594 3279s.gregson

 
 
//

Location

 

LG27Praed StreetSt Mary's Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Bórquez:2016:10.1371/journal.pmed.1002121,
author = {Bórquez, A and Cori, A and Pufall, EL and Kasule, J and Slaymaker, E and Price, A and Elmes, J and Zaba, B and Crampin, AC and Kagaayi, J and Lutalo, T and Urassa, M and Gregson, S and Hallett, TB},
doi = {10.1371/journal.pmed.1002121},
journal = {PLOS Medicine},
title = {The Incidence Patterns Model to Estimate the Distribution of New HIV Infections in Sub-Saharan Africa: Development and Validation of a Mathematical Model.},
url = {http://dx.doi.org/10.1371/journal.pmed.1002121},
volume = {13},
year = {2016}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: Programmatic planning in HIV requires estimates of the distribution of new HIV infections according to identifiable characteristics of individuals. In sub-Saharan Africa, robust routine data sources and historical epidemiological observations are available to inform and validate such estimates. METHODS AND FINDINGS: We developed a predictive model, the Incidence Patterns Model (IPM), representing populations according to factors that have been demonstrated to be strongly associated with HIV acquisition risk: gender, marital/sexual activity status, geographic location, "key populations" based on risk behaviours (sex work, injecting drug use, and male-to-male sex), HIV and ART status within married or cohabiting unions, and circumcision status. The IPM estimates the distribution of new infections acquired by group based on these factors within a Bayesian framework accounting for regional prior information on demographic and epidemiological characteristics from trials or observational studies. We validated and trained the model against direct observations of HIV incidence by group in seven rounds of cohort data from four studies ("sites") conducted in Manicaland, Zimbabwe; Rakai, Uganda; Karonga, Malawi; and Kisesa, Tanzania. The IPM performed well, with the projections' credible intervals for the proportion of new infections per group overlapping the data's confidence intervals for all groups in all rounds of data. In terms of geographical distribution, the projections' credible intervals overlapped the confidence intervals for four out of seven rounds, which were used as proxies for administrative divisions in a country. We assessed model performance after internal training (within one site) and external training (between sites) by comparing mean posterior log-likelihoods and used the best model to estimate the distribution of HIV incidence in six countries (Gabon, Kenya, Malawi, Rwanda, Swaziland, and Zambia) in the region. We subsequ
AU  - Bórquez,A
AU  - Cori,A
AU  - Pufall,EL
AU  - Kasule,J
AU  - Slaymaker,E
AU  - Price,A
AU  - Elmes,J
AU  - Zaba,B
AU  - Crampin,AC
AU  - Kagaayi,J
AU  - Lutalo,T
AU  - Urassa,M
AU  - Gregson,S
AU  - Hallett,TB
DO  - 10.1371/journal.pmed.1002121
PY  - 2016///
SN  - 1549-1277
TI  - The Incidence Patterns Model to Estimate the Distribution of New HIV Infections in Sub-Saharan Africa: Development and Validation of a Mathematical Model.
T2  - PLOS Medicine
UR  - http://dx.doi.org/10.1371/journal.pmed.1002121
UR  - http://hdl.handle.net/10044/1/40304
VL  - 13
ER  -
Download