Early intensification of backyard poultry systems in the tropics: a case study.

Poultry production is an important way of enhancing the livelihoods of rural populations, especially in low- and middle-income countries (LMICs). As poultry production in LMICs remains dominated by backyard systems with low inputs and low outputs, considerable yield gaps exist. Intensification can increase poultry productivity, production and income. This process is relatively recent in LMICs compared to high-income countries. The management practices and the constraints faced by smallholders trying to scale-up their production, in the early stages of intensification, are poorly understood and described. We thus investigated the features of the small-scale commercial chicken sector in a rural area distant from major production centres. We surveyed 111 commercial chicken farms in Kenya in 2016. We targeted farms that sell the majority of their production, owning at least 50 chickens, partly or wholly confined and provided with feeds. We developed a typology of semi-intensive farms. Farms were found mainly to raise dual-purpose chickens of local and improved breeds, in association with crops and were not specialized in any single product or market. We identified four types of semi-intensive farms that were characterized based on two groups of variables related to intensification and accessibility: (i) remote, small-scale old farms, with small flocks, growing a lot of their own feed; (ii) medium-scale, old farms with a larger flock and well located in relation to markets and (iii) large-scale recently established farms, with large flocks, (iii-a) well located and buying chicks from third-party providers and (iii-b) remotely located and hatching their own chicks. The semi-intensive farms we surveyed were highly heterogeneous in terms of size, age, accessibility, management, opportunities and challenges. Farm location affects market access and influences the opportunities available to farmers, resulting in further diversity in farm profiles. The future of these semi-intensive farms could be compromised by several factors, including the competition with large-scale intensive farmers and with importations. Our study suggests that intensification trajectories in rural areas of LMICs are potentially complex, diverse and non-linear. A better understanding of intensification trajectories should, however, be based on longitudinal data. This could, in turn, help designing interventions to support small-scale farmers.

Deterministic processes structure bacterial genetic communities across an urban landscape.

Land-use change is predicted to act as a driver of zoonotic disease emergence through human exposure to novel microbial diversity, but evidence for the effects of environmental change on microbial communities in vertebrates is lacking. We sample wild birds at 99 wildlife-livestock-human interfaces across Nairobi, Kenya, and use whole genome sequencing to characterise bacterial genes known to be carried on mobile genetic elements (MGEs) within avian-borne Escherichia coli (n = 241). By modelling the diversity of bacterial genes encoding virulence and antimicrobial resistance (AMR) against ecological and anthropogenic forms of urban environmental change, we demonstrate that communities of avian-borne bacterial genes are shaped by the assemblage of co-existing avian, livestock and human communities, and the habitat within which they exist. In showing that non-random processes structure bacterial genetic communities in urban wildlife, these findings suggest that it should be possible to forecast the effects of urban land-use change on microbial diversity.

Household socio-economic position and individual infectious disease risk in rural Kenya.

The importance of household socio-economic position (SEP) in shaping individual infectious disease risk is increasingly recognised, particularly in low income settings. However, few studies have measured the extent to which this association is consistent for the range of pathogens that are typically endemic among the rural poor in the tropics. This cross-sectional study assessed the relationship between SEP and human infection within a single community in western Kenya using a set of pathogens with diverse transmission routes. The relationships between household SEP and individual infection with Plasmodium falciparum, hookworm (Ancylostoma duodenale and/or Necator americanus), Entamoeba histolytica/dispar, Mycobacterium tuberculosis, and HIV, and co-infections between hookworm, P. falciparum and E. histolytica/dispar, were assessed using multivariable logistic and multinomial regression. Individuals in households with the lowest SEP were at greatest risk of infection with P. falciparum, hookworm and E. histolytica/dispar, as well as co-infection with each pathogen. Infection with M. tuberculosis, by contrast, was most likely in individuals living in households with the highest SEP. There was no evidence of a relationship between individual HIV infection and household SEP. We demonstrate the existence of a household socio-economic gradient within a rural farming community in Kenya which impacts upon individual infectious disease risk. Structural adjustments that seek to reduce poverty, and therefore the socio-economic inequalities that exist in this community, would be expected to substantially reduce overall infectious disease burden. However, policy makers and researchers should be aware that heterogeneous relationships can exist between household SEP and infection risk for different pathogens in low income settings.

The topology of between-herd cattle contacts in a mixed farming production system in western Kenya.

In many livestock production systems in sub-Saharan Africa, cattle are owned by individual keepers but regularly mix with animals from other herds while grazing communal land, at watering points or through the use of shared bulls for breeding and ploughing. Such contacts may have important implications for disease transmission and control but are not well documented. We describe between-farm contacts in Kimilili sub-county of Bungoma County, a mixed farming area of predominately smallholder farmers. Between-farm contacts occurring during grazing or at shared water points over the past four weeks were captured in seven randomly selected villages using a photo-elicitation tool. The use of shared bulls for breeding and ploughing and cattle introductions from farms within the same village in the past 12 months were also captured. Contact networks were constructed for each contact type in each village. In total 329 farms were included in the study. Networks resembled undirected scale-free graphs with a network density ranging between 9.6 and 14.0. Between 45.6 and 100% of the farms in each study village had been in contact over the past four weeks through grazing and watering contacts. Between 88.9 and 100% were considered to have been in contact over the past 12 months. The topology of the networks was heterogeneous, with some farms exhibiting a high degree of contact. The degree of farm contact and distances between farms were negatively correlated (Pearson correlation coefficient range -0.2 to -0.4). Effective disease control and surveillance must take into consideration the frequency and range of contacts that occur between farms within a single village. Cattle keepers are highly interconnected and pathogens that are transmitted through direct or indirect animal contact would be expected to spread rapidly in the study system. However, the observed heterogeneity in between-farm contact may present opportunities for interventions to be targeted to particular herds to limit infectious disease spread.

Serological evidence of Francisella tularensis in febrile patients seeking treatment at remote hospitals, northeastern Kenya, 2014-2015.

Tularaemia is a highly contagious infectious zoonosis caused by the bacterial agent Francisella tularensis. The aim of this study was to investigate the presence of antibodies to F. tularensis in febrile patients in northeastern Kenya. During 2014-2015, 730 patients were screened for anti-F. tularensis antibodies using a combination of ELISA and Western blot. Twenty-seven (3.7%) individuals were positive for F. tularensis. Tularaemia was not suspected by the treating clinicians in any of them. Our results suggest that tularaemia may be present in Kenya but remain unreported, and emphasizes the need for local clinicians to broaden their diagnostic repertoire when evaluating patients with undifferentiated febrile illness.

Serosurvey of Coxiella burnetii (Q fever) in Dromedary Camels (Camelus dromedarius) in Laikipia County, Kenya.

Dromedary camels (Camelus dromedarius) are an important protein source for people in semi-arid and arid regions of Africa. In Kenya, camel populations have grown dramatically in the past few decades resulting in the potential for increased disease transmission between humans and camels. An estimated four million Kenyans drink unpasteurized camel milk, which poses a disease risk. We evaluated the seroprevalence of a significant zoonotic pathogen, Coxiella burnetii (Q fever), among 334 camels from nine herds in Laikipia County, Kenya. Serum testing revealed 18.6% positive seroprevalence of Coxiella burnetii (n = 344). Increasing camel age was positively associated with C. burnetii seroprevalence (OR = 5.36). Our study confirmed that camels living in Laikipia County, Kenya, have been exposed to the zoonotic pathogen, C. burnetii. Further research to evaluate the role of camels in disease transmission to other livestock, wildlife and humans in Kenya should be conducted.

Environmental predictors of bovine Eimeria infection in western Kenya.

Eimeriosis is caused by a protozoan infection affecting most domestic animal species. Outbreaks in cattle are associated with various environmental factors in temperate climates but limited work has been done in tropical settings. The objective of this work was to determine the prevalence and environmental factors associated with bovine Eimeria spp. infection in a mixed farming area of western Kenya. A total of 983 cattle were sampled from 226 cattle-keeping households. Faecal samples were collected directly from the rectum via digital extraction and analysed for the presence of Eimeria spp. infection using the MacMaster technique. Individual and household level predictors of infection were explored using mixed effects logistic regression. The prevalence of individual animal Eimeria infection was 32.8% (95% CI 29.9-35.9). A positive linear relationship was found between risk of Eimeria infection and increasing temperature (OR = 1.4, 95% CI 1.06-1.86) and distance to areas at risk of flooding (OR = 1.49, 95% CI 1.17-1.91). There was weak evidence of non-linear relationship between Eimeria infection and the proportion of the area around a household that was classified as swamp (OR = 1.12, 95% CI 0.87-1.44; OR (quadratic term) = 0.85, 95% CI 0.73-1.00), and the sand content of the soil (OR = 1.18, 95% CI 0.91-1.53; OR (quadratic term) = 1.1, 95% CI 0.99-1.23). The risk of animal Eimeria spp. infection is influenced by a number of climatic and soil-associated conditions.

Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever.

BACKGROUND: Q fever in Kenya is poorly reported and its surveillance is highly neglected. Standard empiric treatment for febrile patients admitted to hospitals is antimalarials or penicillin-based antibiotics, which have no activity against Coxiella burnetii. This study aimed to assess the seroprevalence and the predisposing risk factors for Q fever infection in febrile patients from a pastoralist population, and derive a model for clinical prediction of febrile patients with acute Q fever. METHODS: Epidemiological and clinical data were obtained from 1067 patients from Northeastern Kenya and their sera tested for IgG antibodies against Coxiella burnetii antigens by enzyme-linked-immunosorbent assay (ELISA), indirect immunofluorescence assay (IFA) and quantitative real-time PCR (qPCR). Logit models were built for risk factor analysis, and diagnostic prediction score generated and validated in two separate cohorts of patients. RESULTS: Overall 204 (19.1 %, 95 % CI: 16.8-21.6) sera were positive for IgG antibodies against phase I and/or phase II antigens or Coxiella burnetii IS1111 by qPCR. Acute Q fever was established in 173 (16.2 %, 95 % CI: 14.1-18.7) patients. Q fever was not suspected by the treating clinicians in any of those patients, instead working diagnosis was fever of unknown origin or common tropical fevers. Exposure to cattle (adjusted odds ratio [aOR]: 2.09, 95 % CI: 1.73-5.98), goats (aOR: 3.74, 95 % CI: 2.52-9.40), and animal slaughter (aOR: 1.78, 95 % CI: 1.09-2.91) were significant risk factors. Consumption of unpasteurized cattle milk (aOR: 2.49, 95 % CI: 1.48-4.21) and locally fermented milk products (aOR: 1.66, 95 % CI: 1.19-4.37) were dietary factors associated with seropositivity. Based on regression coefficients, we calculated a diagnostic score with a sensitivity 93.1 % and specificity 76.1 % at cut off value of 2.90: fever >14 days (+3.6), abdominal pain (+0.8), respiratory tract infection (+1.0) and diarrhoea (-1.1). CONCLUSION: Q fever is common in febrile Kenyan patients but underappreciated as a cause of community-acquired febrile illness. The utility of Q fever score and screening patients for the risky social-economic and dietary practices can provide a valuable tool to clinicians in identifying patients to strongly consider for detailed Q fever investigation and follow up on admission, and making therapeutic decisions.

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya.

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization.

Population-dynamics focussed rapid rural mapping and characterisation of the peri-urban interface of Kampala, Uganda.

In developing countries, cities are rapidly expanding and urban and peri-urban agriculture (UPA) has an important role in feeding these growing urban populations; however such agriculture also carries public health risks such as zoonotic disease transmission. It is important to assess the role of UPA in food security and public health risks to make evidence-based decisions on policies. Describing and mapping the peri-urban interface (PUI) are the essential first steps for such an assessment. Kampala, the capital city of Uganda is a rapidly expanding city where the PUI has not previously been mapped or properly described. In this paper we provide a spatial representation of the entire PUI of Kampala economic zone and determine the socio-economic factors related with peri-urbanicity using a population-dynamics focussed rapid rural mapping. This fills a technical gap of rapid rural mapping and offers a simple and rapid methodology for describing the PUI which can be applied in any city in developing countries for wide range of studies.

Porcine cysticercosis in southeast Uganda: seroprevalence in kamuli and kaliro districts.

The recent recognition of neurocysticercosis as a major cause of epilepsy in Uganda and changes in pig demography have lead to a need to better understand the basic epidemiology of Taenia solium infections in pigs and humans. Human exposure is a function of the size of the animal reservoir of this zoonosis. This is the first field survey for porcine cysticercosis to investigate the prevalence of antigen-positive pigs across an entire rural district of south-east Uganda. In our field surveys, 8.6% of 480 pigs screened were seropositive for the parasite by B158/B60 Ag-ELISA. In addition, of the 528 homesteads surveyed 138 (26%) did not have pit latrines indicating a high probability of pigs having access to human faeces and thus T. solium eggs. This study thus indicates the need for better data on this neglected zoonotic disease in Uganda, with a particular emphasis on the risk factors for infection in both pigs and humans. In this regard, further surveys of pigs, seroprevalence surveys in humans and an understanding of cysticercosis-related epilepsy are required, together with risk-factor studies for human and porcine infections.

Human African trypanosomiasis: Epidemiology and control.

Human African trypanosomiasis (HAT), or sleeping sickness, describes not one but two discrete diseases: that caused by Trypanosoma brucei rhodesiense and that caused by T. b. gambiense. The Gambian form is currently a major public health problem over vast areas of central and western Africa, while the zoonotic, Rhodesian form continues to present a serious health risk in eastern and southern Africa. The two parasites cause distinct clinical manifestations, and there are significant differences in the epidemiology of the diseases caused. We discuss the differences between the diseases caused by the two parasites, with an emphasis on disease burden, reservoir hosts, transmission, diagnosis, treatment and control. We analyse how these differences impacted on historical disease control trends and how they can inform contemporary treatment and control options. We consider the optimal ways in which to devise HAT control policies in light of the differing biology and epidemiology of the parasites, and emphasise, in particular, the wider aspects of control policy, outlining the responsibilities of individuals, governments and international organisations in control programmes.

Central point sampling from cattle in livestock markets in areas of human sleeping sickness.

We present the results of a study to determine the value of central point sampling in cattle markets as a means of estimating the trypanosomiasis (T. brucei s.l.) prevalence in the surrounding landscape in Uganda. We find that in the epidemic area studied, central point sampling is a good predictor of prevalence in surrounding villages, but not in endemic areas. We also find that animals infected with trypanosomiasis are more likely to be brought for sale in livestock markets in endemic areas; we discuss these results in relation to the prevention of the spread of sleeping sickness.

Quantifying the level of under-detection of Trypanosoma brucei rhodesiense sleeping sickness cases.

To formally quantify the level of under-detection of Trypanosoma brucei rhodesiense sleeping sickness (SS) during an epidemic in Uganda, a decision tree (under-detection) model was developed; concurrently, to quantify the subset of undetected cases that sought health care but were not diagnosed, a deterministic (subset) model was developed. The values of the under-detection model parameters were estimated from previously published records of the duration of symptoms prior to presentation and the ratio of early to late stage cases in 760 SS patients presenting at LIRI hospital, Tororo, Uganda during the 1988--1990 epidemic of SS. For the observed early to late stage ratio of 0.47, we estimate that the proportion of under-detection in the catchment area of LIRI hospital was 0.39 (95% CI 0.37--0.41) i.e. 39% of cases are not reported. Based on this value, it is calculated that for every one reported death of SS, 12.0 (95% CI 11.0--13.0) deaths went undetected in the LIRI hospital catchment area - i.e. 92% of deaths are not reported. The deterministic (subset) model structured on the possible routes of a SS infection to either diagnosis or death through the health system or out of it, showed that of a total of 73 undetected deaths, 62 (CI 60-64) (85%) entered the healthcare system but were not diagnosed, and 11 (CI 11--12) died without seeking health care from a recognized health unit. The measure of early to late stage presentation provides a tractable measure to determine the level of rhodesiense SS under-detection and to gauge the effects of interventions aimed at increasing treatment coverage.

A burgeoning epidemic of sleeping sickness in Uganda.

The epidemic of Trypanosoma brucei rhodesiense sleeping sickness in eastern Uganda, which began in 1998 as a result of movements of the livestock reservoir of the parasite, has continued to spread. An additional 133 000 people have been put at risk of infection in Kaberamaido, another newly affected district. The few resources committed to control interventions in Soroti district have failed to contain the epidemic. The high prevalence of the parasite in cattle presents a significant risk for transmission to human beings and further spread of this neglected zoonotic disease. Targeted interventions are urgently needed to control epidemics and reduce the high mortality resulting from sleeping sickness.

The epidemiology of animal bite injuries in Uganda and projections of the burden of rabies.

OBJECTIVES: Rabies is a global problem, although it is often under-reported in developing countries. We aimed at describing the profile of patients presenting to health centres with animal bite injuries in Uganda, and use a predictive model to estimate the mortality of rabies at a national level. METHODS: We conducted a passive surveillance study in Uganda based in a random sample of health centres supplied with rabies vaccine to determine the characteristics of bite injury patients and establish the age and sex profiles of patients, the site of bites and their severity, wound management techniques and details of the vaccination course given. We also applied a decision tree model to the data to estimate the rabies mortality from the bite injury data using an established protocol. RESULTS: We found that most patients are bitten by dogs, and that a considerable proportion of these are young children, who are at greater risk of developing rabies in the absence of treatment due to the location of the bites they receive. From conservative parameter estimates, we estimate that in the absence of post-exposure prophylaxis (PET), 592 (95% CI 345-920) deaths would occur, and that if one dose of PET is sufficient for protection following a rabid animal bite, 20 (95% CI 5-50) deaths would occur annually. If a complete course of PET is required for protection following a rabid animal bite, up to 210 (95% CI 115-359) deaths would occur, as 41% of patients did not complete their course of PET. CONCLUSIONS: Active animal bite surveillance studies are required to improve our mortality estimates and determine the true burden of rabies in the Ugandan population. We emphasize the need for small-scale active case detection studies and improved data on the recognition of rabies in dogs as inputs for improving national-level estimates of rabies mortality.

Spatial and temporal risk factors for the early detection of Trypanosoma brucei rhodesiense sleeping sickness patients in Tororo and Busia districts, Uganda.

We have carried out a study of risk factors for early detection of Trypanosoma brucei rhodesiense sleeping sickness. Records of sleeping sickness patients from 1987 to 2001 from Tororo and Busia districts in Uganda were reviewed for their village of origin and clinical stage (early or late). All villages that reported sleeping sickness and fixed post-diagnostic sleeping sickness health units in Tororo and Busia districts were geo-referenced. The spatial distribution of early and late stage patient detection by health units was analysed using Geographical Information Systems (GIS). Of 1316 sleeping sickness patients admitted at the Livestock Health Research Institute and Busolwe hospitals and Lumino health centre from Tororo and Busia districts, 471 (35.8%) were early stage, 825 (62.7%) were late stage, while 20 (1.5%) were not staged. Five hundred and eighty-five (44.5%) came from within a 10 km radius of the reporting health units. After multivariate analysis, the proportion of early stage patients detected was found to be significantly associated with patients originating from within a 10 km radius of the health unit (P < 0.01), with adults (>19 years) (P < 0.01), and with annual parish incidence (P < 0.01). Application of GIS and the early to late stages ratio are an informative and powerful means of determining efficiency of surveillance of sleeping sickness.

Assessing the patterns of health-seeking behaviour and awareness among sleeping-sickness patients in eastern Uganda.

For those with sleeping sickness, the consequences of delayed diagnosis include poor prognosis at treatment and an increased risk of tsetse infection. Data on their socio-demographic and clinical characteristics, health-seeking behaviour and delays in presentation and diagnosis were collected from 119 diagnosed cases of rhodesiense sleeping sickness in eastern Uganda. The median total delay, from onset of the illness to diagnosis, was 60 days. The median service-provider delay (30 days) was markedly longer than the median patient delay (17 days). Each of these delays was, however, considerable and independently associated with patients presenting with late-stage sleepiness, giving odds ratios and (95% confidence intervals) of 7.29 (3.10-17.14) and 2.98 (1.38-6.43), respectively. A blood examination at the first visit was also associated with the service-provider delay (odds ratio = 0.45; 95% confidence interval = 0.22-0.95). Most of the patients (77.4%) had either been referred to the local sleeping-sickness hospital by other members of their community or presented at the hospital on their own initiative; few had been referred by other components of the local health system. The results are disappointing, not only in showing long delays in diagnosis (and therefore in treatment) but also in indicating that much of the delay is attributable to the service provider failing to diagnose sleeping sickness among symptomatic individuals.