Estimating HIV incidence from age-specific prevalence data: comparison with concurrent cohort estimates in a study of male factory workers, Harare, Zimbabwe.
OBJECTIVE
To compare HIV incidence estimates from cross-sectional age-specific prevalence data with concurrent cohort estimates and to examine the sensitivity of the estimates to changes in age-categorization and survivorship assumptions.


METHODS
Two previously described methods of estimating HIV incidence from cross-sectional prevalence data - the cumulative incidence and survival (CIS) and constant prevalence (CP) methods - are applied using data from a study of male factory workers in Harare, Zimbabwe. The methods are applied under two alternative groupings of the HIV prevalence data and under alternative survivorship assumptions: (a) Weibull distribution providing the best fit to the HIV prevalence data using the CIS method; (b) Weibull distribution matching data from an HIV natural history cohort study in Uganda; and (c) survivorship pattern as in (b) with survival periods reducing with increasing age at infection. Age-specific, age-standardized and cumulative HIV incidence estimates are calculated. The results are compared with concurrent longitudinal estimates from 3 years of follow-up of the Harare cohort (1993-1995).


RESULTS
Age-standardized HIV incidence was estimated at 2.02 per 100 man years (95% CI, 1.57-2.47) in the cohort study. There was evidence of recent variability in HIV incidence in these data. Estimates from the cross-sectional methods ranged from 1.98 to 2.74 per 100 man years and were sensitive to changes in age-categorization of the HIV prevalence data and changes in survivorship assumptions. The cross-sectional estimates were higher at central ages and lower at older ages than the cohort estimates. The age-specific estimates from the CIS method were less sensitive to changes in age grouping than those from the CP method.


CONCLUSIONS
HIV incidence remains high in Harare. Incidence estimates broadly consistent with cohort estimates can be obtained from single-round cross-sectional HIV prevalence data in established epidemics - even when the underlying assumption of stable endemic prevalence is not fully met. Estimates based on cross-sectional surveys should therefore be explored when reliable longitudinal estimates cannot be obtained. More data on post-HIV infection survivorship distributions in sub-Saharan Africa would facilitate the improvement of estimates of incidence based on cross-sectional surveys.
Gregson
S
S
Wellcome Trust Centre for the Epidemiology of Infectious Disease, Zoology Department, Oxford University, UK.
Machekano
R
R
Donnelly
C A
CA
Mbizvo
M T
MT
Anderson
R M
RM
Katzenstein
D A
DA
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D015658:HIV Infections / Q000453:epidemiology*; D015528:HIV Seroprevalence; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015030:Zimbabwe / Q000453:epidemiology