High prevalence of <i>Rickettsia africae</i> variants in <i>Amblyomma variegatum</i> ticks from domestic mammals in rural western Kenya: implications for human health

Tick-borne spotted fever group (SFG) rickettsioses are emerging human diseases caused by obligate intracellular Gram-negative bacteria of the genus Rickettsia. Despite being important causes of systemic febrile illnesses in travelers returning from sub-Saharan Africa, little is known about the reservoir hosts of these pathogens. We conducted surveys for rickettsiae in domestic animals and ticks in a rural setting in western Kenya. Of the 100 serum specimens tested from each species of domestic ruminant 43% of goats, 23% of sheep, and 1% of cattle had immunoglobulin G (IgG) antibodies to the SFG rickettsiae. None of these sera were positive for IgG against typhus group rickettsiae. We detected Rickettsia africae–genotype DNA in 92.6% of adult Amblyomma variegatum ticks collected from domestic ruminants, but found no evidence of the pathogen in blood specimens from cattle, goats, or sheep. Sequencing of a subset of 21 rickettsia-positive ticks revealed R. africae variants in 95.2% (20/21) of ticks tested. Our findings show a high prevalence of R. africae variants in A. variegatum ticks in western Kenya, which may represent a low disease risk for humans. This may provide a possible explanation for the lack of African tick-bite fever cases among febrile patients in Kenya

Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.publishedVersio

High Prevalence of Rickettsia africae Variants in Amblyomma variegatum Ticks from Domestic Mammals in Rural Western Kenya: Implications for Human Health

Tick-borne spotted fever group (SFG) rickettsioses are emerging human diseases caused by obligate intracellular Gram-negative bacteria of the genus Rickettsia. Despite being important causes of systemic febrile illnesses in travelers returning from sub-Saharan Africa, little is known about the reservoir hosts of these pathogens. We conducted surveys for rickettsiae in domestic animals and ticks in a rural setting in western Kenya. Of the 100 serum specimens tested from each species of domestic ruminant 43% of goats, 23% of sheep, and 1% of cattle had immunoglobulin G (IgG) antibodies to the SFG rickettsiae. None of these sera were positive for IgG against typhus group rickettsiae. We detected Rickettsia africae–genotype DNA in 92.6% of adult Amblyomma variegatum ticks collected from domestic ruminants, but found no evidence of the pathogen in blood specimens from cattle, goats, or sheep. Sequencing of a subset of 21 rickettsia-positive ticks revealed R. africae variants in 95.2% (20/21) of ticks tested. Our findings show a high prevalence of R. africae variants in A. variegatum ticks in western Kenya, which may represent a low disease risk for humans. This may provide a possible explanation for the lack of African tick-bite fever cases among febrile patients in Kenya

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

Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies