Critique of early models of the demographic impact of HIV/AIDS in sub-Saharan Africa based on contemporary empirical data from Zimbabwe.
Early mathematical models varied in their predictions of the impact of HIV/AIDS on population growth from minimal impact to reductions in growth, in pessimistic scenarios, from positive to negative values over a period of 25 years. Models predicting negative rates of natural increase forecast little effect on the dependency ratio. Twenty years later, HIV prevalence in small towns, estates, and rural villages in eastern Zimbabwe, has peaked within the intermediate range predicted by the early models, but the demographic impact has been more acute than was predicted. Despite concurrent declines in fertility, fueled in part by HIV infections (total fertility is now 8% lower than expected without an epidemic), and a doubling of the crude death rate because of HIV/AIDS, the rate of natural population increase between 1998 and 2005 remained positive in each socioeconomic stratum. In the worst-affected areas (towns with HIV prevalence of 33%), HIV/AIDS reduced growth by two-thirds from 2.9% to 1.0%. The dependency ratio fell from 1.21 at the onset of the HIV epidemic to 0.78, the impact of HIV-associated adult mortality being outweighed by fertility decline. With the benefit of hindsight, the more pessimistic early models overestimated the demographic impact of HIV epidemics by overextrapolating initial HIV growth rates or not allowing for heterogeneity in key parameters such as transmissibility and sexual risk behavior. Data collected since the late 1980s show that there was a mismatch between the observed growth in the HIV epidemic and assumptions made about viral transmission.
Gregson
Simon
S
Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, United Kingdom. s.gregson@imperial.ac.uk
Nyamukapa
Constance
C
Lopman
Ben
B
Mushati
Phyllis
P
Garnett
Geoffrey P
GP
Chandiwana
Stephen K
SK
Anderson
Roy M
RM
D000163:Acquired Immunodeficiency Syndrome / Q000453:epidemiology*; D000293:Adolescent; D000328:Adult; D000353:Africa, Southern / Q000453:epidemiology; D003710:Demography; D003906:Developing Countries; D005260:Female; D015658:HIV Infections / Q000401:mortality* / Q000635:transmission; D006679:HIV Seropositivity; D015528:HIV Seroprevalence; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011153:Population; D011157:Population Dynamics; D012106:Research; D012107:Research Design; D012424:Rural Population / Q000706:statistics & numerical data; D012959:Socioeconomic Factors; D014505:Urban Population / Q000706:statistics & numerical data; D015030:Zimbabwe / Q000453:epidemiology