Research ArticleHIV

Effect of population viral load on prospective HIV incidence in a hyperendemic rural African community

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Science Translational Medicine  13 Dec 2017:
Vol. 9, Issue 420, eaam8012
DOI: 10.1126/scitranslmed.aam8012
  • Fig. 1 Age-sex differences in viral load patterns in the 2011 population-based survey.

    Geometric mean viral load (A) and proportion of viral loads (>50,000 copies/ml) (B). Estimates are shown with 95% confidence intervals.

  • Fig. 2 Geographical variations in the population viral load (PVL) indices derived from the 2011 population-based viral load survey.

    The PVL indices are derived from HIV-positive participants only (A to C) and both HIV-positive and HIV-negative participants (D to F). The PVL maps were obtained using a moving two-dimensional standard Gaussian kernel of a 3-km radius. The Kulldorff spatial clusters of high viral loads are shown as black circles. Further details of the Kulldorff spatial clustering results are given in table S9. VL, viral load.

  • Fig. 3 Graphs showing the ecological relationship between HIV incidence (2011 to 2015) and PVL quartiles derived from the 2011 population–based viral load survey (quartile 1, communities with lowest PVL values).

    The PVL indices are derived from HIV-positive participants only (A to C) and HIV-positive and HIV-negative participants (D to F). Incidence estimates are shown with 95% confidence intervals.

  • Table 1 Summary statistics: Viral load measurements by sex and age for the population-based survey data (2011).

    CI, confidence interval.

    SamplesGeometric mean>50,000 copies/ml
    N(%)Copies/ml(95% CI)Proportion(95% CI)
    Overall2,4208,259(7,507–9,087)0.26(0.24–0.27)
    Male506(20.91)12,802(10,352–15,832)0.34(0.3–0.38)
    Female1,914(79.09)7,356(6,614–8,181)0.24(0.22–0.26)
    Age group
      15–98(4.05)25,139(15,934–39,660)0.46(0.36–0.56)
      20–295(12.19)13,508(10,376–17,586)0.29(0.24–0.35)
      25–405(16.74)10,815(8,606–13,592)0.28(0.24–0.33)
      30–360(14.88)7,766(6,026–10,008)0.26(0.22–0.31)
      35–353(14.59)8,460(6,529–10,963)0.28(0.23–0.32)
      40–243(10.04)6,823(5,026–9,262)0.23(0.18–0.29)
      45+666(27.52)5,238(4,397–6,240)0.19(0.16–0.22)
  • Table 2 Seroincidence rates per 100 person-years for the HIV cohort of repeat-testers who were HIV-uninfected at baseline (2011).
    Person-yearsEventsIncidence per
    100 person-years
    Rate95% CI
    Population HIV prevalence
      0–14.9%935181.92(1.21–3.05)
      15–24.9%15,6934803.06(2.8–3.34)
      25+%9,5903613.76(3.4–4.17)
    Sex
      Male9,9431941.95(1.69–2.25)
      Female16,2766654.09(3.79–4.41)
    Age strata
      15–9,2282572.78(2.46–3.15)
      20–4,9982875.74(5.11–6.45)
      25–2,8381575.53(4.73–6.47)
      30–1,727663.82(3–4.86)
      35–1,735291.67(1.16–2.4)
      40–2,015301.49(1.04–2.13)
      45+3,675330.9(0.64–1.26)
    Area of residence
      Rural16,8175103.03(2.78–3.31)
      Peri-urban8,3813123.72(3.33–4.16)
      Urban1,019373.63(2.63–5.01)
    Marital status
      Single15,0784482.97(2.71–3.26)
      Married
    monogamous
    5,8981963.32(2.89–3.82)
      Married
    polygamous
    5,2422154.1(3.59–4.69)
    Number of partners in the last 12 months
      Zero11,8273282.77(2.49–3.09)
      One10,2913683.58(3.23–3.96)
      More than one4,1001633.98(3.41–4.64)
    Household wealth quintile
      Poorest5,3482013.76(3.27–4.31)
      2nd poorest5,4281633.00(2.58–3.5)
      3rd poorest5,9322183.67(3.22–4.2)
      4th poorest5,3631723.21(2.76–3.72)
      Wealthiest4,1461052.53(2.09–3.07)
    N = 8,732
  • Table 3 Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition and three PVL measures.

    The PVL measures were derived from the HIV-positive cases (models A1 to A3) and the HIV-positive and HIV-negative cases (models B1 to B3) of a population-based survey. The full output is given in tables S1 and S2. Model 1 shows the unadjusted hazard ratios (HRs) for the PVL measures; model 2 shows these HRs after adjusting for age, sex, urban status, marital status, number of sexual partners in the last year, and household wealth; model 3 shows these HRs after adjusting for the model 2 covariates as well as HIV prevalence.

    Geometric mean viral load*Prevalence detectable
    viremia
    Community transmission
    index
    HR(95% CI)PHR(95% CI)PHR(95% CI)P
    Population-based: HIV-positive cases only
      Model A1: Unadjusted HR1.000(1.000–1.000)0.2540.997(0.987–1.007)0.5030.999(0.957–1.043)0.966
      Model A2: Adjusted HR without HIV prevalence1.000(1.000–1.000)0.8171.005(0.994–1.015)0.4011.037(0.990–1.086)0.130
      Model A3: Adjusted HR with HIV prevalence1.000(1.000–1.000)0.4751.008(0.996–1.019)0.1901.048(0.999–1.100)0.057
    Population-based: HIV-positive and HIV-negative cases
      Model B1: Unadjusted HR1.049(1.029–1.069)<0.0011.053(1.030–1.078)<0.0011.187(1.103–1.277)<0.001
      Model B2: Adjusted HR without HIV prevalence1.079(1.046–1.113)<0.0011.070(1.039–1.103)<0.0011.224(1.121–1.337)<0.001
      Model B3: Adjusted HR with HIV prevalence1.091(1.045–1.138)<0.0011.063(1.025–1.103)0.0011.193(1.079–1.320)0.001
    N8,7328,7328,732

    *For a one unit increase in geometric mean viral load.

    †For a 1% increase in the prevalence of detectable viremia.

    ‡For a predicted one transmission event increase per 100 sexual contacts.

    • Table 4 Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for females and the three male PVL measures.

      The PVL measures were derived from the HIV-positive males (models A1 to A3) and the HIV-positive and HIV-negative males (models B1 to B3) of a population-based survey. The full output is given in tables S3 and S5. Model 1 shows the unadjusted HRs for the PVL measures; model 2 shows these HRs after adjusting for age, sex, urban status, marital status, number of sexual partners in the last year, and household wealth; model 3 shows these HRs after adjusting for the model 2 covariates as well as HIV prevalence.

      HIV acquisition risk for femalesGeometric mean viral load*Prevalence detectable
      viremia
      Community transmission
      index
      HR(95% CI)PHR(95% CI)PHR(95% CI)P
      Population-based: HIV-positive males only
        Model A1: Unadjusted HR1.000(1.000–1.000)0.3931.000(0.994–1.006)0.9830.999(0.987–1.012)0.922
        Model A2: Adjusted HR without HIV prevalence1.000(1.000–1.000)0.8361.001(0.995–1.008)0.6621.003(0.991–1.016)0.599
        Model A3: Adjusted HR with HIV prevalence1.000(1.000–1.000)0.4871.003(0.996–1.011)0.3931.004(0.990–1.018)0.563
      Population-based: HIV-positive and HIV-negative males
        Model B1: Unadjusted HR1.066(1.021–1.113)0.0041.039(1.016–1.063)0.0011.100(1.033–1.171)0.003
        Model B2: Adjusted HR without HIV prevalence1.056(1.006–1.108)0.0271.039(1.013–1.066)0.0031.095(1.025–1.171)0.007
        Model B3: Adjusted HR with HIV prevalence1.160(1.058–1.271)0.0011.061(1.020–1.104)0.0041.110(1.015–1.214)0.022
      N5,1885,1885,188

      *For a one unit increase in geometric mean viral load.

      †For a 1% increase in the prevalence of detectable viremia.

      ‡For a predicted one transmission event increase per 100 sexual contacts.

      • Table 5 Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for males and the three female PVL measures.

        The PVL measures were derived from the HIV-positive females (models A1 to A3) and the HIV-positive and HIV-negative females (models B1 to B3) of a population-based survey. The full output is given in tables S4 and S6. Model 1 shows the unadjusted HRs for the PVL measures; model 2 shows these HRs after adjusting for age, sex, urban status, marital status, number of sexual partners in the last year, and household wealth; model 3 shows these HRs after adjusting for the model 2 covariates as well as HIV prevalence.

        HIV acquisition risk for malesGeometric mean viral load*Prevalence detectable
        viremia
        Community transmission
        index
        HR(95% CI)PHR(95% CI)PHR(95% CI)P
        Population-based: HIV-positive females only
          Model A1: Unadjusted HR1.000(1.000–1.000)0.1760.985(0.965–1.007)0.1740.977(0.920–1.037)0.439
          Model A2: Adjusted HR without HIV prevalence1.000(1.000–1.000)0.9890.997(0.972–1.021)0.7821.010(0.943–1.082)0.773
          Model A3: Adjusted HR with HIV prevalence1.000(1.000–1.000)0.9850.997(0.971–1.024)0.8091.008(0.933–1.088)0.845
        Population-based: HIV-positive and HIV-negative females
          Model B1: Unadjusted HR1.057(1.030–1.084)<0.0011.109(1.052–1.170)<0.0011.405(1.174–1.683)<0.001
          Model B2: Adjusted HR without HIV prevalence1.053(1.009–1.099)0.0181.077(1.004–1.155)0.0391.270(1.020–1.581)0.032
          Model B3: Adjusted HR with HIV prevalence1.081(1.009–1.158)0.0281.106(1.001–1.221)0.0481.452(1.057–1.997)0.021
        N3,5443,5443,544

        *For a one unit increase in geometric mean viral load.

        †For a 1% increase in the prevalence of detectable viremia.

        ‡For a predicted one transmission event increase per 100 sexual contacts.

        Supplementary Materials

        • www.sciencetranslationalmedicine.org/cgi/content/full/9/420/eaam8012/DC1

          Fig. S1. Proportion of facility-based viral loads of >50,000 copies/ml by sex for the year 2011.

          Table S1. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between risk of HIV acquisition and three PVL measures constructed from the HIV-positive cases of a population-based survey.

          Table S2. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between risk of HIV acquisition and three PVL measures constructed from the HIV-positive and HIV-negative cases of a population-based survey.

          Table S3. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for females and the three PVL measures.

          Table S4. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for males and the three PVL measures.

          Table S5. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for females and the three PVL measures.

          Table S6. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for males and the three PVL measures.

          Table S7. Summary statistics: Viral load measurements by sex and age for the routine facility–based data.

          Table S8. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between risk of HIV acquisition and three PVL measures constructed from the routine facility–based survey data.

          Table S9. Kulldorff spatial clustering results for the PVL measures shown in Fig. 2.

        • Supplementary Material for:

          Effect of population viral load on prospective HIV incidence in a hyperendemic rural African community

          Frank Tanser,* Alain Vandormael, Diego Cuadros, Andrew N. Phillips, Tulio de Oliveira, Andrew Tomita, Till Bärnighausen, Deenan Pillay

          *Corresponding author. Email: ftanser{at}gmail.com

          Published 13 December 2017, Sci. Transl. Med. 9, eaam8012 (2017)
          DOI: 10.1126/scitranslmed.aam8012

          This PDF file includes:

          • Fig. S1. Proportion of facility-based viral loads of >50,000 copies/ml by sex for the year 2011.
          • Table S1. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between risk of HIV acquisition and three PVL measures constructed from the HIV-positive cases of a population-based survey.
          • Table S2. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between risk of HIV acquisition and three PVL measures constructed from the HIV-positive and HIV-negative cases of a population-based survey.
          • Table S3. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for females and the three PVL measures.
          • Table S4. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for males and the three PVL measures.
          • Table S5. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for females and the three PVL measures.
          • Table S6. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between the risk of HIV acquisition for males and the three PVL measures.
          • Table S7. Summary statistics: Viral load measurements by sex and age for the routine facility–based data.
          • Table S8. Results of the multivariable analysis (Cox proportional hazard model) to examine the relationship between risk of HIV acquisition and three PVL measures constructed from the routine facility–based survey data.
          • Table S9. Kulldorff spatial clustering results for the PVL measures shown in Fig. 2.

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