The epidemiology of HIV infection in Zambia

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Kandala, Ngianga-Bakwin ; Ji, Chen ; Cappuccio, Francesco ; Stones, R. Willliam (2008)

Abstract Population surveys of health and fertility are an important source of information about demographic trends and their likely impact on the HIV/AIDS epidemic. In contrast to groups sampled at health facilities they can provide nationally and regionally representative estimates of a range of variables. Data on HIV sero-status were collected in the 2001-2 Zambia Demographic and Health Survey (ZDHS) and made available in a separate data file in which HIV status was linked to a very limited set of demographic variables. We utilized this data set to examine associations between HIV prevalence, gender, age and geographical location by using the generalized geo-additive semi-parametric model as an alternative to the common linear model in HIV research. 54 % of the overall sample of 3950 was female. The overall HIV positivity rate was 565 (14.3%). The mean age at HIV diagnosis for male was 30.3 (SD: 11.2) and 27.7 (SD: 9.3) for female respectively. Lusaka and Copperbelt have the first and second highest prevalence of AIDS/HIV (marginal odds ratios of 3.24 and 2.88 respectively) but when the younger age of the urban population and the spatial auto-correlation was taken into account Lusaka and Copper belt were no longer among the areas with the highest prevalence. Nonlinear effects of age at HIV diagnosis were also discussed and the importance of spatial residual effects and control of confounders on the prevalence of HIV infection. Controlling for important risk factors such as geographical location, age structure of the population, gender gave estimates of prevalence that are statistically robust. Researchers should be encouraged to use all available information in the data to account for important risk factors when reporting AIDS/HIV prevalence. Where this is not possible, correction factors should be applied, particularly where estimates of AIDS/HIV prevalence are pooled in systematic reviews. Our maps can be used for policy planning and management of AIDS/HIV in Zambia. N-B.Kandala@warwick.ac.uk (Kandala, Ngianga-Bakwin) Chen.Ji@warwick.ac.uk (Ji, Chen) F.P.Cappuccio@warwick.ac.uk (Cappuccio, Francesco P) r.w.stones@soton.ac.uk (Stones, William) Warwick Medical School, Clinical Sciences Research Institute - Clifford Road Bridge--> , Walsgrave Hospital--> - CV2 2DX - Coventry - UNITED KINGDOM (Kandala, Ngianga-Bakwin) Warwick Medical School, Clinical Sciences Research Institute - Coventry - UNITED KINGDOM (Ji, Chen) Warwick Medical School, Clinical Sciences Research Institute - Coventry - UNITED KINGDOM (Cappuccio, Francesco P) University of Southampton, Centre for AIDS Research - Southampton - UNITED KINGDOM (Stones, William) UNITED KINGDOM
  • References (17)
    17 references, page 1 of 2

    1. Rural Poverty Portal, (2006). Rural poverty in Zambia. Available from [http://www.ruralpovertyportal.org/english/regions/africa/zmb/index.htm] (accessed Sept 18 Nov 2007).

    2. UNAIDS/WHO Epidemiological Fact Sheet - 2004 Update, Zambia

    3. UNAIDS/WHO 2006 Report on the global AIDS epidemic

    4. UNAIDS/WHO 2006 Report on the global AIDS epidemic F

    5. Zambia Summary Country Profile for HIV/AIDS Treatment Scale-up, o WHO/UNAIDS, July 2004

    6. Central Statistrical Office Republic of Zambia, Central Board of Health Republic of Zambia, Macro O. PZambia Demographic and Health Survey 2001-2. Calverton, Maryland. USA: Central Statistical Office, Central Board of Health and ORC Macro, e 2003 e

    7. Central Statistical Office , (2004). [http://www.cpc.unc.edu]. Zambia Sexual Behaviour r Survey 2003. 2004; Available from [http://www.cpc.unc.edu/measure/publications/index.php] (accessed 16 Nov 2006). R

    8. Fahrmeir L, Lang S., 2001. BayesieanInference for Generalized Additive Mixed Models Based on Markov Random Field Pvriors. Applied Statistics (JRSS C). 50: 201- 220. i

    9. Kandala N-B: Bayesian Geo-additive modelleing of Childhood morbidity in Malawi. Applied Stochastic Models in Business and Industwry,2006; 22:139-154.

    10. Kandala N-B, C Ji , N Stallard , S Stranges & FP Cappuccio. Spatial Analysis of Risk Factors for Childhood Morbidity in Nigeria. AmOerican Journal of Tropical Medicine & Hygiene, 2007 (In press). n

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