Survey on prevalence and risk factors on HIV-1 among pregnant women in North-Rift, Kenya: a hospital based cross-sectional study conducted between 2005 and 2006

<p>Abstract</p> <p>Background</p> <p>The HIV/AIDS epidemic in Kenya is a major public-health problem. Estimating the prevalence of HIV in pregnant women provides essential information for an effective implementation of HIV/AIDS control measures and monitoring of HIV spread within a country. The objective of this study was to determine the prevalence of HIV infection, risk factors for HIV/AIDS and immunologic (lymphocyte profile) characteristics among pregnant women attending antenatal clinics in three district hospitals in North-Rift, Kenya.</p> <p>Methods</p> <p>Blood samples were collected from pregnant women attending antenatal clinics in three district hospitals (Kitale, Kapsabet and Nandi Hills) after informed consent and pre-test counseling. The samples were tested for HIV antibodies as per the guidelines laid down by Ministry of Health, Kenya. A structured pretested questionnaire was used to obtain demographic data. Lymphocyte subset counts were quantified by standard flow cytometry.</p> <p>Results</p> <p>Of the 4638 pregnant women tested, 309 (6.7%) were HIV seropositive. The majority (85.1%) of the antenatal attendees did not know their HIV status prior to visiting the clinic for antenatal care. The highest proportion of HIV infected women was in the age group 21–25 years (35.5%). The 31–35 age group had the highest (8.5%) HIV prevalence, while women aged more than 35 years had the lowest (2.5%).</p> <p>Women in a polygamous relationship were significantly more likely to be HIV infected as compared to those in a monogamous relationship (p = 0.000). The highest HIV prevalence (6.3%) was recorded among antenatal attendees who had attended secondary schools followed by those with primary and tertiary level of education (6% and 5% respectively). However, there was no significant relationship between HIV seropositivity and the level of education (p = 0.653 and p = 0.469 for secondary and tertiary respectively). The mean CD4 count was 466 cells/mm³(9–2000 cells/mm³). Those that had less than 200 cells/mm³accounted for 14% and only nine were on antiretroviral therapy.</p> <p>Conclusion</p> <p>Seroprevalence of HIV was found to be consistent with the reports from the national HIV sentinel surveys. Enumeration of T-lymphocyte (CD4/8) should be carried out routinely in the antenatal clinics for proper timing of initiation of antiretroviral therapy among HIV infected pregnant women.</p

Estimating HIV Incidence among Adults in Kenya and Uganda: A Systematic Comparison of Multiple Methods

CITATION: Kim, A. A. et al. 2011. Estimating HIV incidence among adults in Kenya and Uganda : a systematic comparison of multiple methods. PLos ONE, 6(3): e17535, doi:10.1371/journal.pone.0017535.The original publication is available at http://journals.plos.org/plosoneBackground: Several approaches have been used for measuring HIV incidence in large areas, yet each presents specific challenges in incidence estimation. Methodology/Principal Findings: We present a comparison of incidence estimates for Kenya and Uganda using multiple methods: 1) Epidemic Projections Package (EPP) and Spectrum models fitted to HIV prevalence from antenatal clinics (ANC) and national population-based surveys (NPS) in Kenya (2003, 2007) and Uganda (2004/2005); 2) a survey-derived model to infer age-specific incidence between two sequential NPS; 3) an assay-derived measurement in NPS using the BED IgG capture enzyme immunoassay, adjusted for misclassification using a locally derived false-recent rate (FRR) for the assay; (4) community cohorts in Uganda; (5) prevalence trends in young ANC attendees. EPP/Spectrum-derived and survey-derived modeled estimates were similar: 0.67 [uncertainty range: 0.60, 0.74] and 0.6 [confidence interval: (CI) 0.4, 0.9], respectively, for Uganda (2005) and 0.72 [uncertainty range: 0.70, 0.74] and 0.7 [CI 0.3, 1.1], respectively, for Kenya (2007). Using a local FRR, assay-derived incidence estimates were 0.3 [CI 0.0, 0.9] for Uganda (2004/2005) and 0.6 [CI 0, 1.3] for Kenya (2007). Incidence trends were similar for all methods for both Uganda and Kenya. Conclusions/Significance: Triangulation of methods is recommended to determine best-supported estimates of incidence to guide programs. Assay-derived incidence estimates are sensitive to the level of the assay's FRR, and uncertainty around high FRRs can significantly impact the validity of the estimate. Systematic evaluations of new and existing incidence assays are needed to the study the level, distribution, and determinants of the FRR to guide whether incidence assays can produce reliable estimates of national HIV incidence.http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017535Publisher's versio

Trends in HIV prevalence among young women aged 15–24 years attending antenatal clinics in Kenya, 2000–2005.

<p>Trends in HIV prevalence among young women aged 15–24 years attending antenatal clinics in Kenya, 2000–2005.</p

Trends in HIV incidence by estimation method, adults aged 15–49 years, Uganda, 2000–2007.

<p>Trends in HIV incidence by estimation method, adults aged 15–49 years, Uganda, 2000–2007.</p

HIV Incidence and Prevalence Rates in the Year of the National Survey, by Estimation Method, Kenya and Uganda.

<p>*Among adults aged 15–49 years.</p><p>†Uganda 2000–2005.</p><p>‡Kenya 2003–2007.</p><p>a. All assay-derived estimates were weighted to account for unequal probability of selection and adjusted for non-response, where necessary. For the 2007 Kenya estimate, any participant that reported current ARV use was excluded from the incidence analysis.</p><p>b. The Uganda FRR was generated from: (1) pooled data from 699 ARV naive long-term specimens from Rakai (76/473) and rural Tororo districts (28/226) in Uganda that classified as false-recent on the BED assay.</p><p>c. Statistically significant difference observed in assay-derived estimate and the EPP/Spectrum estimate in Uganda 2005.</p><p>d. No data published on community cohort incidence in Kenya.</p><p>e. 95% confidence intervals not reported.</p><p>f. All participants that that reported current ARV use were excluded from the 2007 Kenya HIV prevalence estimate.</p

Trends in HIV prevalence among young women aged 15–24 years attending antenatal clinics in Uganda, 2000–2007.

<p>Trends in HIV prevalence among young women aged 15–24 years attending antenatal clinics in Uganda, 2000–2007.</p