Malaria Parasitemia Among Febrile Patients Seeking Clinical Care at an Outpatient Health Facility in an Urban Informal Settlement Area in Nairobi, Kenya

Thinking clearly about social aspects of infectious disease transmission

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

Carriage of antimicrobial-resistant bacteria in a high-density informal settlement in Kenya is associated with environmental risk-factors

BACKGROUND

The relationship between antibiotic use and antimicrobial resistance varies with cultural, socio-economic, and environmental factors. We examined these relationships in Kibera, an informal settlement in Nairobi-Kenya, characterized by high population density, high burden of respiratory disease and diarrhea.

METHODS

Two-hundred households were enrolled in a 5-month longitudinal study. One adult (≥ 18 years) and one child (≤ 5 years) participated per household. Biweekly interviews (n = 1516) that included questions on water, sanitation, hygiene, and antibiotic use in the previous two weeks were conducted, and 2341 stool, 2843 hand swabs and 1490 drinking water samples collected. Presumptive E. coli (n = 34,042) were isolated and tested for susceptibility to nine antibiotics.

RESULTS

Eighty percent of presumptive E. coli were resistant to ≥ 3 antibiotic classes. Stool isolates were resistant to trimethoprim (mean: 81%), sulfamethoxazole (80%), ampicillin (68%), streptomycin (60%) and tetracycline (55%). Ninety-seven households reported using an antibiotic in at least one visit over the study period for a total of 144 episodes and 190 antibiotic doses. Enrolled children had five times the number of episodes reported by enrolled adults (96 vs. 19). Multivariable linear mixed-effects models indicated that children eating soil from the household yard and the presence of informal hand-washing stations were associated with increased numbers of antimicrobial-resistant bacteria (counts increasing by 0·27-0·80 log₁₀ and 0·22-0·51 log₁₀ respectively, depending on the antibiotic tested). Rainy conditions were associated with reduced carriage of antimicrobial-resistant bacteria (1·19 to 3·26 log₁₀ depending on the antibiotic tested).

CONCLUSIONS

Antibiotic use provided little explanatory power for the prevalence of antimicrobial resistance. Transmission of resistant bacteria in this setting through unsanitary living conditions likely overwhelms incremental changes in antibiotic use. Under such circumstances, sanitation, hygiene, and disease transmission are the limiting factors for reducing the prevalence of resistant bacteria.

Falciparum Malaria in Febrile Patients at Sentinel Sites for Influenza Surveillance in the Central African Republic from 2015 to 2018

Malaria is a major public health issue in the Central African Republic (CAR) despite massive scale-up of malaria interventions. However, no information is available on the incidence of malaria in febrile illness cases or on the distribution of malaria infection according to demographic characteristics, which are important indicators and valuable epidemiological surveillance tools. This study therefore aimed to characterize malaria in the network of sentinel sites set up for influenza surveillance. A retrospective analysis was conducted to explore the data from these sentinel sites from 2015 to 2018. The Paracheck-Pf® rapid diagnosis test kit was used to screen for malaria in febrile illness cases. A total of 3609 malaria cases were identified in 5397 febrile patients, giving an incidence rate of 66.8%. The age group of 1–4 years was the most affected by malaria (76.0%). Moreover, prevalence varied across different sentinel sites, with the Bossembele Health Center, located in a rural area, showing an incidence of 96%, the Saint Joseph Health Center in a semiurban area of Bangui showing an incidence of 75%, and the Bangui Pediatric Complex in an urban site with an incidence of only 44.6%. Malaria transmission was holoendemic over the four-year study period, and malaria incidence decreased from 2016 to 2018. The incidence of malaria coinfection with influenza was 6.8%. This study demonstrated clear microspatial heterogeneity of malaria. Malaria was consistently the most frequent cause of febrile illness. Including sites in different climate zones in the CAR will allow for a more representative study.

Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya

BACKGROUND

Spatial and temporal malaria risk maps are essential tools to monitor the impact of control, evaluate priority areas to reorient intervention approaches and investments in malaria endemic countries. Here, the analysis of 36 years data on Plasmodium falciparum prevalence is used to understand the past and chart a future for malaria control in Kenya by confidently highlighting areas within important policy relevant thresholds to allow either the revision of malaria strategies to those that support pre-elimination or those that require additional control efforts.

METHODS

Plasmodium falciparum parasite prevalence (PfPR) surveys undertaken in Kenya between 1980 and 2015 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2-10 years (PfPR2-10) at 1 × 1 km spatial resolution from 1990 to 2015. Changing PfPR2-10 was compared against plausible explanatory variables. The fitted model was used to categorize areas with varying degrees of prediction probability for two important policy thresholds PfPR2-10 < 1% (non-exceedance probability) or ≥ 30% (exceedance probability).

RESULTS

5020 surveys at 3701 communities were assembled. Nationally, there was an 88% reduction in the mean modelled PfPR2-10 from 21.2% (ICR: 13.8-32.1%) in 1990 to 2.6% (ICR: 1.8-3.9%) in 2015. The most significant decline began in 2003. Declining prevalence was not equal across the country and did not directly coincide with scaled vector control coverage or changing therapeutics. Over the period 2013-2015, of Kenya's 47 counties, 23 had an average PfPR2-10 of < 1%; four counties remained ≥ 30%. Using a metric of 80% probability, 8.5% of Kenya's 2015 population live in areas with PfPR2-10 ≥ 30%; while 61% live in areas where PfPR2-10 is < 1%.

CONCLUSIONS

Kenya has made substantial progress in reducing the prevalence of malaria over the last 26 years. Areas today confidently and consistently with < 1% prevalence require a revised approach to control and a possible consideration of strategies that support pre-elimination. Conversely, there remains several intractable areas where current levels and approaches to control might be inadequate. The modelling approaches presented here allow the Ministry of Health opportunities to consider data-driven model certainty in defining their future spatial targeting of resources.

Fever in Common Infectious Diseases

Infection of the upper airways is very common and is the most common acute illness evaluated in the outpatient setting. The infection is usually caused by viruses including rhinoviruses, influenza viruses, parainfluenza and respiratory syncytial viruses. Influenza is the only viral infection preventable by vaccination and occurs predominately during annual winter epidemics. Bacterial infection such as acute rhinopharyngitis is uncommon and usually presents with either persistent symptoms of an URTI lasting over a week or worsening course after initial improvement or acute onset with high fever and inflammatory changes confined to the pharynx.

Fever is common in both bacterial and viral gastroenteritis. High fever is commonly present in many bacterial causes (e.g. Shigella, Salmonella, Shiga toxin-producing E. coli). Fever is often absent or low-grade in other diseases (e.g. enteropathogenic E. coli, cholera). Other febrile conditions cause diarrhoea and need to be differentiated.

Fever in CNS infection is the most common presenting symptom in children beyond the neonatal age owing to the presence of inflammatory mediators, particularly IL-1 and TNF in the blood or within the CNS. In MCD, fever was the first symptom in children younger than 5 years and 94% developed fever at some point.

Viral exanthems are common causes of febrile illness in children. More than 50 viral agents are known to cause a rash. Historically, exanthems were numbered in the order in which they were differentiated from other exanthems. Thus the first was measles; second, scarlet fever; third, rubella; forth, so-called Filatov-Dukes disease (no longer recognized as an entity); fifth, erythema infectiosum; and sixth, exanthema subitum. As more exanthems were described, numerical assignment became impractical.

The Relationship Between Invasive Nontyphoidal Salmonella Disease, Other Bacterial Bloodstream Infections, and Malaria in Sub-Saharan Africa

BACKGROUND

Country-specific studies in Africa have indicated that Plasmodium falciparum is associated with invasive nontyphoidal Salmonella (iNTS) disease. We conducted a multicenter study in 13 sites in Burkina Faso, Ethiopia, Ghana, Guinea-Bissau, Kenya, Madagascar, Senegal, South Africa, Sudan, and Tanzania to investigate the relationship between the occurrence of iNTS disease, other systemic bacterial infections, and malaria.

METHODS

Febrile patients received a blood culture and a malaria test. Isolated bacteria underwent antimicrobial susceptibility testing, and the association between iNTS disease and malaria was assessed.

RESULTS

A positive correlation between frequency proportions of malaria and iNTS was observed (P = .01; r = 0.70). Areas with higher burden of malaria exhibited higher odds of iNTS disease compared to other bacterial infections (odds ratio [OR], 4.89; 95% CI, 1.61-14.90; P = .005) than areas with lower malaria burden. Malaria parasite positivity was associated with iNTS disease (OR, 2.44; P = .031) and gram-positive bacteremias, particularly Staphylococcus aureus, exhibited a high proportion of coinfection with Plasmodium malaria. Salmonella Typhimurium and Salmonella Enteritidis were the predominant NTS serovars (53/73; 73%). Both moderate (OR, 6.05; P = .0001) and severe (OR, 14.62; P < .0001) anemia were associated with iNTS disease.

CONCLUSIONS

A positive correlation between iNTS disease and malaria endemicity, and the association between Plasmodium parasite positivity and iNTS disease across sub-Saharan Africa, indicates the necessity to consider iNTS as a major cause of febrile illness in malaria-holoendemic areas. Prevention of iNTS disease through iNTS vaccines for areas of high malaria endemicity, targeting high-risk groups for Plasmodium parasitic infection, should be considered.