HIV / AIDS Epidemic in the Democratic Republic of the Congo: Current Level of Key Indicators and Projection by 2030
Central African Journal of Public Health
Volume 4, Issue 3, June 2018, Pages: 86-94
Received: Jul. 4, 2018;
Accepted: Jul. 17, 2018;
Published: Aug. 14, 2018
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Jack Hyyombo Tambwe Kokolomami, Department of Epidemiology and Biostatistics, Kinshasa School of Public Health, University of Kinshasa, Kinshasa, the Democratic Republic of Congo
Patrick Kalambayi Kayembe, Department of Epidemiology and Biostatistics, Kinshasa School of Public Health, University of Kinshasa, Kinshasa, the Democratic Republic of Congo
Since 2015, the Democratic Republic of Congo has subscribed to the global goal of eliminating HIV / AIDS as a major public health problem by 2030. However, there is a lack of evidence on the current level of key indicators of the epidemic in the country. In another hand, no study has yet explored the extent to which the country could meet the 2030 target. This study aimed to: (i) determine the current level of key indicators of the HIV / AIDS epidemic in the DRC; and (ii) assess whether the DRC could achieve the goal of eliminating HIV / AIDS as a major public health problem by the end of 2030. For the country as a whole and for 24 of its 26 provinces, we performed: (1) a trend analysis of HIV / AIDS surveillance data; and (2) projections of key indicators of the epidemic by 2030 on Spectrum software. In 2017, the DRC is experiencing a generalized epidemic of HIV / AIDS (national prevalence of 1.0%, 97.5%CI: 0.85% - 1.14%), which is poorly expansive (national incidence of 6, 97, 5%IC: 5 - 7 new infections per 10 000 person-years of observation). Ten of its 26 provinces have so far experienced a concentrated epidemic. From 2017 to 2030, HIV prevalence will decline for the country as a whole, falling below 1% by 2018 and reaching 0.76% by 2030. The incidence will experience the same overall declining trend. Nine provinces appear to be driving the epidemic. The DRC should target intensively the nine driving provinces of the epidemic and some of its key determinants, in order to fulfill the goal of reducing the HIV / AIDS epidemic to a non-major public health concern by 2030.
Jack Hyyombo Tambwe Kokolomami,
Patrick Kalambayi Kayembe,
HIV / AIDS Epidemic in the Democratic Republic of the Congo: Current Level of Key Indicators and Projection by 2030, Central African Journal of Public Health.
Vol. 4, No. 3,
2018, pp. 86-94.
JM M, Francis H, TC Q, et al. Hiv seroprevalence among hospital workers in Kinshasa, Zaire: Lack of association with occupational exposure. JAMA [Internet]. 1986 Dec 12;256(22):3099–102.
Mulanga-Kabeya C, Nzilambi N, Edidi B, Minlangu M, Tshimpaka T, Kambembo L, et al. Evidence of stable HIV seroprevalences in selected populations in the Democratic Republic of the Congo. AIDS. 1998;12(8):905–10.
Yang C, Dash B, Hanna SL, Frances HS, Nzilambi N, Colebunders RC, et al. Predominance of HIV type 1 subtype G among commercial sex workers from Kinshasa, Democratic Republic of Congo. AIDS Res Hum Retroviruses. 2001;17(4):361–5.
Mokili JL, Rogers M, Carr JK, Simmonds P, Bopopi JM, Foley BT, et al. Identification of a novel clade of human immunodeficiency virus type 1 in Democratic Republic of Congo. AIDS Res Hum Retroviruses. 2002;18(11):817–23.
Worobey M, Gemmel M, Teuwen DE, Haselkorn T, Bunce M, Muyembe J, et al. Direct Evidence of Extensive Diversity of HIV-1 in Kinshasa by 1960. 2013;455(7213):661–4.
Vidal N, Bazepeo SE, Mulanga C, Delaporte E, Peeters M. Genetic characterization of eight full-length HIV type 1 genomes from the Democratic Republic of Congo (DRC) reveal a new subsubtype, A5, in the A radiation that predominates in the recombinant structure of CRF26_A5U. AIDS Res Hum Retroviruses. 2009;25(8):823–32.
Yang C, Li M, Mokili JLK, Winter J, Lubaki NM, Mwandagalirwa KM, et al. Genetic diversification and recombination of HIV type 1 group M in Kinshasa, Democratic Republic of Congo. AIDS Res Hum Retroviruses. 2005;21(7):661–6.
Faria NR, Rambaut A, Suchard MA, Baele G, Bedford T, Ward MJ, et al. The early spread and epidemic ignition of HIV-1 in human populations. 2015;346(6205):56–61.
Vidal N, Peeters M, Mulanga-Kabeya C, Nzilambi N, Robertson D, Ilunga W, et al. Unprecedented Degree of Human Immunodeficiency Virus Type 1 (HIV-1) Group M Genetic Diversity in the Democratic Republic of Congo Suggests that the HIV-1 Pandemic Originated in Central Africa. J Virol. 2000;74(22):10498–507.
Rodgers MA, Wilkinson E, Vallari A, McArthur C, Sthreshley L, Brennan CA, et al. Sensitive Next-Generation Sequencing Method Reveals Deep Genetic Diversity of HIV-1 in the Democratic Republic of the Congo. J Virol [Internet]. 2017;91(6):1–18.
Stover J, Johnson P, Zaba B, Zwahlen M, Dabis F, Ekpini RE. The Spectrum projection package : improvements in estimating mortality, ART needs, PMTCT impact and uncertainty bounds The Spectrum projection package : improvements in estimating mortality, ART needs, PMTCT impact and uncertainty bounds; 2008
Abramson JH. Abramson, J. H. WINPEPI updated: computer programs for epidemiologists, and their teaching potential. Epidemiologic Perspectives & Innovations; 2011.
Bao L. A new infectious disease model for estimating and projecting HIV/AIDS epidemics. Sex Transm Infect [Internet]. 2012;88:i58-64.
MPSMRM M. Enquête Démographique et de Santé en République Démocratique du Congo 2013-2014. Rockville, Maryland, USA : MPSMRM, MSP et ICF International; 2014.
Cuadros DF, Awad SF, Abu-Raddad LJ. Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa. Int J Health Geogr [Internet]. International Journal of Health Geographics; 2013;12(1):28.
Granich R, Gupta S, Hersh B, Williams B, Montaner J, Young B, et al. Trends in AIDS deaths, new infections and ART coverage in the top 30 countries with the highest AIDS mortality burden; 1990-2013. PLoS One. 2015;10(7):1–16.
Chow EPF, Wilson DP, Zhang L. Estimating HIV incidence among female partners of bisexual men in China. Int J Infect Dis. International Society for Infectious Diseases; 2012 May;16(5):e312-20.
Cleghorn FR, Jack N, Murphy JR, Edwards J, Mahabir B, Paul R, et al. Direct and indirect estimates of HIV-1 incidence in a high-prevalence population. Am J Epidemiol. 1998;147(9):834–9.
Hallett TB, Stover J, Mishra V, Ghys PD, Gregson S. Europe PMC Funders Group Estimates of HIV incidence from household-based prevalence surveys. 2013;24(1):147–52.
Zaba B, Boerma T, White R. Monitoring the AIDS epidemic using HIV prevalence data among young women attending antenatal clinics: prospects and problems. AIDS. 2000 Jul;14(11):1633–45.
R JC, Becker NG. Estimating HIV incidence using dates of both HIV and AIDS diagnoses. 2000;(July 1999):1165–77.
Remis RS, Palmer RWH. Testing bias in calculating HIV incidence from the Serologic Testing Algorithm for Recent HIV Seroconversion. AIDS. 2009 Mar;23(4):493–503.
Brookmeyer R. Should biomarker estimates of HIV incidence be adjusted ? 2009;(December 2008):485–91.
Gouws E, Mishra V, Fowler TB. Comparison of adult HIV prevalence from national population-based surveys and antenatal clinic surveillance in countries with generalised epidemics: implications for calibrating surveillance data. Sex Transm Infect. 2008 Aug;84 Suppl 1:i17–23.
UNAIDS. Rapport ONUSIDA sur l’épidémie mondiale du Sida, 2011. Geneva; 2011.
UNAIDS & WHO. Aids Epidemic Update: December 2007. Geneva; 2007.
Brookmeyer R. Measuring the HIV/AIDS epidemic: Approaches and challenges. Epidemiol Rev. 2010;32(1):26–37.
Adilo TM, Wordofa HM. Prevalence of fertility desire and its associated factors among 15- to 49-year-old people living with HIV / AIDS in Addis Ababa, Ethiopia : a cross-sectional study design. HIV/AIDS - Res Palliat Care. 2017;167–76.
Shafer LA, Biraro S, Nakiyingi-Miiro J, Kamali A, Ssematimba D, Ouma J, et al. HIV prevalence and incidence are no longer falling in southwest Uganda: evidence from a rural population cohort 1989-2005. AIDS. 2008 Aug;22(13):1641–9.
Kim A, Hallett T, Stover J, Gouws E, Musinguzi J, Mureithi PK, et al. Estimating HIV incidence among adults in Kenya and Uganda: a systematic comparison of multiple methods. PLoS One [Internet]. 2011 Jan [cited 2013 Mar 17]; 6(3):e17535.