HIV incidence in 3 years of follow-up of a Zimbabwe cohort--1998-2000 to 2001-03: contributions of proximate and underlying determinants to transmission.
BACKGROUND
In recent years, HIV prevalence has begun to decline in Zimbabwe, which has been associated with reductions in sexual risk behaviour. Here, we analyse the determinants of HIV incidence in this period of decline and estimate the population-level impact of identified risk factors.


METHODS
A population-based cohort of 1672 HIV-negative adult males and 2465 HIV-negative adult females was recruited between 1998 and 2000. Each individual was then followed-up 3 years later. The influence and inter-relationship of social, behavioural and demographic variables were examined using a proximate determinants framework. To explore the population-level influence of a variable, methods were developed for estimating a risk factor's contribution to the reproductive number (CRN).


RESULTS
HIV incidence was 19.9 [95% confidence interval (CI) 16.3-24.2] per 1000 person years in men and 15.7 (95% CI 13.0-18.9) in women. Multiple sexual partners, having an unwell partner, and reporting another sexually transmitted disease were risk factors that captured the main aspects of the proximate determinants framework: individual behaviour, partnership characteristics and the probability of transmission, respectively. If the proximate determinants fully captured risk of HIV infection, underlying factors would not influence a fully parameterized model. However, a number of underlying social and demographic determinants remained important in regression models after including the proximate determinants. For both sexes, having multiple sexual partners made a substantial CRN, but, for women, no behaviour explained more than 10% of new infections.


CONCLUSIONS
The proximate determinants did not explain the majority of new infections at the population level. This may be because we have been unable to measure some risks, but identifying risk factors assumes that those acquiring infections are somehow different from others who do not acquire infections. That they are not suggests that in this generalized epidemic there is little difference in readily identifiable characteristics of the individual between those who acquire infection and those who do not.
Lopman
Ben
B
Department of Infectious Disease Epidemiology, Imperial College London, UK.
Nyamukapa
Constance
C
Mushati
Phyllis
P
Mupambireyi
Zivai
Z
Mason
Peter
P
Garnett
Geoff P
GP
Gregson
Simon
S
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D001294:Attitude to Health; D015331:Cohort Studies; D016001:Confidence Intervals; D003906:Developing Countries; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D015658:HIV Infections / Q000453:epidemiology* / Q000635:transmission*; D018023:HIV Seronegativity; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D016012:Poisson Distribution; D011159:Population Surveillance; D011336:Probability; D012189:Retrospective Studies; D018570:Risk Assessment; D012309:Risk-Taking; D017678:Sex Distribution; D012747:Sexual Partners; D015030:Zimbabwe / Q000453:epidemiology