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Modelling changes in HIV prevalence among women attending antenatal clinics in Uganda

Study investigating HIV prevalence changes among women in Uganda using mathematical models and demographic data to inform public health strategies.

johnmwalker
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Modelling changes in HIV prevalence among women attending antenatal clinics in Uganda

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  1. Modelling changes in HIV prevalence among women attending antenatal clinics in Uganda Brian Williams

  2. b N d I l S / N I S I m I m S =birth rate N=S +I  = rate at which new infections occur  = mortality The basic model

  3. R0 = 3.3

  4. b N d I l S / N I S I I m S Normal (Weibull 2) = birth rate N=S +I  = infection rate I= Weibull mortality Exponential (Weibull 1)

  5. ~ I b d N l S / N I I S m I m S  = birth rate N = population  = e–P I = Weibull mort. e –P ~ Heterogeneity in sexual behaviour

  6. b d N I l I S / N I S I m S  = birth rate N = population • = C(t) I= mortality ~ ~ ~ ~ C(t) Including control

  7. b d N I l I S / N I S I m S ~ * –aM  = birth rate N = population • = e I= mortality e ~ –aM * Mortality leads to behaviour change

  8. Nairobi 6 yr Nunn P et al. Tuberculosis control in the era of HIV. Nat Rev Immunol. 2005 Oct;5(10):819-26.

  9. TB incidence among gold miners in SA Corbett EL Stable incidence rates of tuberculosis (TB) among human immunodeficiency virus (HIV)-negative South African gold miners during a decade of epidemic HIV-associated TB. J Infect Dis. 2003;188: 1156-63.

  10. SS+ Tuberculosis Prevalence Incidence Disease Duration (%) (%/yr) (yr) HIV+ 0.44 (0.02-1.05) 2.87 (1.94-4.25) 0.15 (0.05-0.48) HIV- 0.55 (0.14–0.95) 0.48 (0.27-0.84) 1.15 (0.48-1.13) DDR = 0.13 (0.09–0.20) Gold miners in South Africa We define disease duration as prevalence divided by incidence

  11. TB-HIV model Repeat the model 4 times, once for each stage of HIV. Use time series of HIV prevalence to determine incidence. Incidence gives rate at which people enter first stage; overall (Weibull) survival determines rate at which people move to next stage. Williams BG et al.The impact of HIV/AIDS on the control of tuberculosis in India. PNAS 2005 102: 9619-9624.

  12. Impact of interventions on TB cases in Kenya Baseline ARV 80% TLTI (6 m) TLTI (life) ARV 100% TB detect. TB cure HIV incid . 800 600 400 200 0 TB incidence/100k/yr Base line: CDR = 50% CR = 70% Interventions: 1% increase 1980 2000 2020 2040 Year Currie, C. et al. Cost, affordability and cost-effectiveness of strategies to control tuberculosis in countries with high HIV prevalence. BMC, 2005. 5: 130.

  13. Percent Percent HIV positive HIV negative Williams BG et al. HIV Infection, Antiretroviral Therapy, and CD4+ Cell Count Distributions in African Populations. J Infect Dis, 2006 194: 1450-8.

  14. 2,000 Initial CD4/mL 1,000 20 10 Time to death (yrs) 2,000 Initial CD4/mL 1,000 20 10 Time to death (yrs)  Model 1 CD4 decline independent of starting value Survival determined by pre-infection CD4  Model 2 Survival independent of starting value CD4 decline determine entirely by starting value and survival distribution

  15. Spatial Epidemiology of HIV Doubling time = 1 year Life expectancy = 10 years Number of partners = 4 Proportion of random partners chosen at random = 0 (left hand set) or 10% (right hand set) in the following slides. Note that in this model migrants have exactly the same sexual behaviour and individual risk as non-migrants.

  16. Questions for all of us • Can we combine spatial/network models with our more conventional continuous time models of HIV? • Can we get a better understanding of the host-viral interaction? • What are the population level implications of 2? • Do we have enough data to explore fully the joint dynamics of TB and HIV?

  17. Advice to young epidemiologists Never make a calculation until you know the answer. Make an estimate before every calculation, try a simple biological argument (R0, generation time, selection, survival, control). Guess the answer to every puzzle. Courage: no one else needs to know what the guess is. Therefore, make it quickly, by instinct. A right guess reinforces this instinct. A wrong guess brings the refreshment of surprise. In either case, life as an epidemiologist, however long, is more fun. Plagiarised from E.F. Taylor and J.A. Wheeler Space-time Physics 1963

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