A seven-year surveillance of epidemiology of malaria reveals travel and gender are the key drivers of dispersion of drug resistant genotypes in Kenya.

Malaria drug resistance is a global public health concern. Though parasite mutations have been associated with resistance, other factors could influence the resistance. A robust surveillance system is required to monitor and help contain the resistance. This study established the role of travel and gender in dispersion of chloroquine resistant genotypes in malaria epidemic zones in Kenya. A total of 1,776 individuals presenting with uncomplicated malaria at hospitals selected from four malaria transmission zones in Kenya between 2008 and 2014 were enrolled in a prospective surveillance study assessing the epidemiology of malaria drug resistance patterns. Demographic and clinical information per individual was obtained using a structured questionnaire. Further, 2 mL of blood was collected for malaria diagnosis, parasitemia quantification and molecular analysis. DNA extracted from dried blood spots collected from each of the individuals was genotyped for polymorphisms in Plasmodium falciparum chloroquine transporter gene (Pfcrt 76), Plasmodium falciparum multidrug resistant gene 1 (Pfmdr1 86 and Pfmdr1 184) regions that are putative drug resistance genes using both conventional polymerase chain reaction (PCR) and real-time PCR. The molecular and demographic data was analyzed using Stata version 13 (College Station, TX: StataCorp LP) while mapping of cases at the selected geographic zones was done in QGIS version 2.18. Chloroquine resistant (CQR) genotypes across gender revealed an association with chloroquine resistance by both univariate model (p = 0.027) and by multivariate model (p = 0.025), female as reference group in both models. Prior treatment with antimalarial drugs within the last 6 weeks before enrollment was associated with carriage of CQR genotype by multivariate model (p = 0.034). Further, a significant relationship was observed between travel and CQR carriage both by univariate model (p = 0.001) and multivariate model (p = 0.002). These findings suggest that gender and travel are significantly associated with chloroquine resistance. From a gender perspective, males are more likely to harbor resistant strains than females hence involved in strain dispersion. On the other hand, travel underscores the role of transport network in introducing spread of resistant genotypes, bringing in to focus the need to monitor gene flow and establish strategies to minimize the introduction of resistance strains by controlling malaria among frequent transporters.

Five-year tracking of Plasmodium falciparum allele frequencies in a holoendemic area with indistinct seasonal transitions.

BACKGROUND: The renewed malaria eradication efforts require an understanding of the seasonal patterns of frequency of polymorphic variants in order to focus limited funds productively. Although cross-sectional studies in holoendemic areas spanning a single year could be useful in describing parasite genotype status at a given point, such information is inadequate in describing temporal trends in genotype polymorphisms. For Plasmodium falciparum isolates from Kisumu District Hospital, Plasmodium falciparum chloroquine-resistance transporter gene (Pfcrt-K76T) and P. falciparum multidrug resistance gene 1 (PfMDR1-N86Y), were analyzed for polymorphisms and parasitemia changes in the 53 months from March 2008 to August 2012. Observations were compared with prevailing climatic factors, including humidity, rainfall, and temperature. METHODS: Parasitemia (the percentage of infected red blood cells per total red blood cells) was established by microscopy for P. falciparum malaria-positive samples. P. falciparum DNA was extracted from whole blood using a Qiagen DNA Blood Mini Kit. Single nucleotide polymorphism identification at positions Pfcrt-K76T and PfMDR1-N86Y was performed using real-time polymerase chain reaction and/or sequencing. Data on climatic variables were obtained from http://www.tutiempo.net/en/. RESULTS: A total of 895 field isolates from 2008 (n=169), 2009 (n=161), 2010 (n=216), 2011 (n=223), and 2012 (n=126) showed large variations in monthly frequency of PfMDR1-N86Y and Pfcrt-K76T as the mutant genotypes decreased from 68.4%±15% and 38.1%±13% to 29.8%±18% and 13.3%±9%, respectively. The mean percentage of parasitemia was 2.61%±1.01% (coefficient of variation 115.86%; n=895). There was no correlation between genotype or parasitemia and climatic factors. CONCLUSION: This study shows variability in the frequency of Pfcrt-K76T and PfMDR1-N86Y polymorphisms during the study period, bringing into focus the role of cross-sectional studies in describing temporal genotype trends. The lack of correlation between genotypes and climatic changes, especially precipitation, emphasizes the cost of investment in genotype change.

Molecular characterization and phylogenetic analysis of the hemagglutinin 1 protein of human influenza A virus subtype H1N1 circulating in Kenya during 2007-2008.

BACKGROUND: Among influenza viruses, type A viruses exhibit the greatest genetic diversity, infect the widest range of host species, and cause the vast majority of cases of severe disease in humans, including cases during the great pandemics. The hemagglutinin 1 (HA1) domain of the HA protein contains the highest concentration of epitopes and, correspondingly, experiences the most intense positive selection pressure. OBJECTIVES: We sought to isolate and genetically characterize influenza A virus subtype H1N1 (A[H1N1]) circulating in Kenya during 2007-2008, using the HA1 protein. METHODS: Nasopharyngeal swab specimens were collected from patients aged ≥ 2 months who presented to 8 healthcare facilities in Kenya with influenza-like illness. We tested specimens for seasonal influenza A viruses, using real-time reverse-transcription polymerase chain reaction (RT-PCR). Viruses were subtyped using subtype-specific primers. Specimens positive for seasonal A(H1N1) were inoculated onto Madin-Darby canine kidney cells for virus isolation. Viral RNAs were extracted from isolates, and the HA1 gene was amplified by RT-PCR, followed by nucleotide sequencing. Nucleotide sequences were assembled using BioEdit and translated into amino acid codes, using DS Gene, version 1.5. Multiple sequence alignments were performed using MUSCLE, version 3.6, and phylogenetic analysis was performed using MrBayes software. RESULTS: We found that, similar to A/Brisbane/59/2007 (H1N1)-like virus, which was included in the southern hemisphere vaccine for the 2009 influenza season, all 2007 Kenyan viruses had D39N, R77K, T132V, K149R, and E277K amino acid substitutions, compared with A/Solomon Islands/3/2006 (H1N1)-like virus, a component of the southern hemisphere vaccine for the 2008 influenza season. However, the majority of 2008 viruses from Kenya also had R192K and R226Q substitutions, compared with A/Solomon Islands/3/2006 (H1N1)-like virus. These 2 changes occurred at the receptor binding site. The majority of the 2008 Kenyan isolates contained N187S, G189N, and A193T mutations, which differed from A/Brisbane/59/2007 (H1N1)-like virus. The A193T substitution is involved in binding the sialic acid receptor. Phylogenetically, the 2008 Kenyan isolates grouped into 2 clusters. The main cluster contained viruses with N187S and A193T changes; residue 187 is involved in receptor binding, whereas residue 193 is at antigenic site Sb. CONCLUSION: Overall, the major genetic variations that occurred in seasonal A(H1) viruses either affected receptor binding or altered epitopes at the immunodominant sites. These genetic variations in seasonal A(H1N1) isolated in Kenya during 2007-2008 highlight the importance of continuing surveillance and characterization of emerging drift variants of influenza virus in Africa.

A growing global network's role in outbreak response: AFHSC-GEIS 2008-2009.

A cornerstone of effective disease surveillance programs comprises the early identification of infectious threats and the subsequent rapid response to prevent further spread. Effectively identifying, tracking and responding to these threats is often difficult and requires international cooperation due to the rapidity with which diseases cross national borders and spread throughout the global community as a result of travel and migration by humans and animals. From Oct.1, 2008 to Sept. 30, 2009, the United States Department of Defense's (DoD) Armed Forces Health Surveillance Center Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) identified 76 outbreaks in 53 countries. Emerging infectious disease outbreaks were identified by the global network and included a wide spectrum of support activities in collaboration with host country partners, several of which were in direct support of the World Health Organization's (WHO) International Health Regulations (IHR) (2005). The network also supported military forces around the world affected by the novel influenza A/H1N1 pandemic of 2009. With IHR (2005) as the guiding framework for action, the AFHSC-GEIS network of international partners and overseas research laboratories continues to develop into a far-reaching system for identifying, analyzing and responding to emerging disease threats.