Estimating the burden of multiple endemic diseases and health conditions using Bayes’ Theorem: A conditional probability model applied to UK dairy cattle

The Global Burden of Animal Diseases (GBADs) is an international collaboration aiming, in part, to measure and improve societal outcomes from livestock. One GBADs objective is to estimate the economic impact of endemic diseases in livestock. However, if individual disease impact estimates are linearly aggregated without consideration for associations among diseases, there is the potential to double count impacts, overestimating the total burden. Accordingly, the authors propose a method to adjust an array of individual disease impact estimates so that they may be aggregated without overlap. Using Bayes’ Theorem, conditional probabilities were derived from inter-disease odds ratios in the literature. These conditional probabilities were used to calculate the excess probability of disease among animals with associated conditions, or the probability of disease overlap given the odds of coinfection, which were then used to adjust disease impact estimates so that they may be aggregated. The aggregate impacts, or the yield, fertility, and mortality gaps due to disease, were then attributed and valued, generating disease-specific losses. The approach was illustrated using an example dairy cattle system with input values and supporting parameters from the UK, with 13 diseases and health conditions endemic to UK dairy cattle: cystic ovary, disease caused by gastrointestinal nematodes, displaced abomasum, dystocia, fasciolosis, lameness, mastitis, metritis, milk fever, neosporosis, paratuberculosis, retained placenta, and subclinical ketosis. The diseases and conditions modelled resulted in total adjusted losses of £ 404/cow/year, equivalent to herd-level losses of £ 60,000/year. Unadjusted aggregation methods suggested losses 14–61% greater. Although lameness was identified as the costliest condition (28% of total losses), variations in the prevalence of fasciolosis, neosporosis, and paratuberculosis (only a combined 22% of total losses) were nearly as impactful individually as variations in the prevalence of lameness. The results suggest that from a disease control policy perspective, the costliness of a disease may not always be the best indicator of the investment its control warrants; the costliness rankings varied across approaches and total losses were found to be surprisingly sensitive to variations in the prevalence of relatively uncostly diseases. This approach allows for disease impact estimates to be aggregated without double counting. It can be applied to any livestock system in any region with any set of endemic diseases, and can be updated as new prevalence, impact, and disease association data become available. This approach also provides researchers and policymakers an alternative tool to rank prevention priorities

Critical linkages between livestock production, livestock trade and potential spread of human African trypanosomiasis in Uganda:Bioeconomic herd modeling and livestock trade analysis

Background: Tsetse-transmitted human African trypanosomiasis (HAT) remains endemic in Uganda. The chronic form caused by Trypanosoma brucei gambiense (gHAT) is found in north-western Uganda, whereas the acute zoonotic form of the disease, caused by T. b. brucei rhodesiense (rHAT), occurs in the eastern region. Cattle is the major reservoir of rHAT in Uganda. These two forms of HAT are likely to converge resulting in a public health disaster. This study examines the intricate and intrinsic links between cattle herd dynamics, livestock trade and potential risk of spread of rHAT northwards. Methods: A bio-economic cattle herd model was developed to simulate herd dynamics at the farm level. Semi-structured interviews (n = 310), focus group discussions (n = 9) and key informant interviews (n = 9) were used to evaluate livestock markets (n = 9) as part of the cattle supply chain analysis. The cattle market data was used for stochastic risk analysis. Results: Cattle trade in eastern and northern Uganda is dominated by sale of draft and adult male cattle as well as exportation of young male cattle. The study found that the need to import draft cattle at the farm level was to cover deficits because of the herd structure, which is mostly geared towards animal traction. The importation and exportation of draft cattle and disposal of old adult male cattle formed the major basis of livestock movement and could result in the spread of rHAT northwards. The risk of rHAT infected cattle being introduced to northern Uganda from the eastern region via cattle trade was found to be high (i.e. probability of 1). Conclusion: Through deterministic and stochastic modelling of cattle herd and cattle trade dynamics, this study identifies critical links between livestock production and trade as well as potential risk of rHAT spread in eastern and northern Uganda. The findings highlight the need for targeted and routine surveillance and control of zoonotic diseases such as rHAT

Livestock network analysis for rhodesiense human African trypanosomiasis control in Uganda

Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness.Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (Ro 1.287), hence regarded as “slow” pathogen, and evaluate the effects of disease interventions.Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network.Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public–private partnerships, would serve to limit its spread

Costs Of Using “Tiny Targets” to Control Glossina fuscipes fuscipes, a Vector of Gambiense Sleeping Sickness in Arua District of Uganda

Introduction To evaluate the relative effectiveness of tsetse control methods, their costs need to be analysed alongside their impact on tsetse populations. Very little has been published on the costs of methods specifically targeting human African trypanosomiasis. Methodology/Principal Findings In northern Uganda, a 250 km2 field trial was undertaken using small (0.5 X 0.25 m) insecticide-treated targets (“tiny targets”). Detailed cost recording accompanied every phase of the work. Costs were calculated for this operation as if managed by the Ugandan vector control services: removing purely research components of the work and applying local salaries. This calculation assumed that all resources are fully used, with no spare capacity. The full cost of the operation was assessed at USD 85.4 per km2, of which USD 55.7 or 65.2% were field costs, made up of three component activities (target deployment: 34.5%, trap monitoring: 10.6% and target maintenance: 20.1%). The remaining USD 29.7 or 34.8% of the costs were for preliminary studies and administration (tsetse surveys: 6.0%, sensitisation of local populations: 18.6% and office support: 10.2%). Targets accounted for only 12.9% of the total cost, other important cost components were labour (24.1%) and transport (34.6%). Discussion Comparison with the updated cost of historical HAT vector control projects and recent estimates indicates that this work represents a major reduction in cost levels. This is attributed not just to the low unit cost of tiny targets but also to the organisation of delivery, using local labour with bicycles or motorcycles. Sensitivity analyses were undertaken, investigating key prices and assumptions. It is believed that these costs are generalizable to other HAT foci, although in more remote areas, with denser vegetation and fewer people, costs would increase, as would be the case for other tsetse control techniques

Daily Fermented Whey Consumption Alters the Fecal Short-Chain Fatty Acid Profile in Healthy Adults

FUNDING: This study was funded by A.Vogel Bioforce AG, Roggwil, Switzerland. NS was co-funded by the School of Medicine, Medical Sciences and Nutrition (University of Aberdeen) and A.Vogel Bioforce AG. The Rowett Institute (University of Aberdeen) receives financial support from the Scottish Government Rural and Environmental Sciences and Analytical Services (RESAS). ACKNOWLEDGMENTS: We thank all the volunteers which contributed their time and efforts into enrolling and completing the trial. Further, we are grateful for the staff at the Human Nutrition Unit and Analytical Services at the Rowett Institute for supporting the research and assisting when needed. We would like to thank Brennan Martin at the Center for Genome Enabled Biology of Medicine for his assistance in DNA sequencingPeer reviewedPublisher PD

The cost of tsetse control using 'Tiny Targets' in the sleeping sickness endemic forest area of Bonon in Côte d'Ivoire: Implications for comparing costs across different settings.

BACKGROUND Work to control the gambiense form of human African trypanosomiasis (gHAT), or sleeping sickness, is now directed towards ending transmission of the parasite by 2030. In order to supplement gHAT case-finding and treatment, since 2011 tsetse control has been implemented using Tiny Targets in a number of gHAT foci. As this intervention is extended to new foci, it is vital to understand the costs involved. Costs have already been analysed for the foci of Arua in Uganda and Mandoul in Chad. This paper examines the costs of controlling Glossina palpalis palpalis in the focus of Bonon in Côte d'Ivoire from 2016 to 2017. METHODOLOGY/PRINCIPAL FINDINGS Some 2000 targets were placed throughout the main gHAT transmission area of 130 km2 at a density of 14.9 per km2. The average annual cost was USD 0.5 per person protected, USD 31.6 per target deployed of which 12% was the cost of the target itself, or USD 471.2 per km2 protected. Broken down by activity, 54% was for deployment and maintenance of targets, 34% for tsetse surveys/monitoring and 12% for sensitising populations. CONCLUSIONS/SIGNIFICANCE The cost of tsetse control per km2 of the gHAT focus protected in Bonon was more expensive than in Chad or Uganda, while the cost per km2 treated, that is the area where the targets were actually deployed, was cheaper. Per person protected, the Bonon cost fell between the two, with Uganda cheaper and Chad more expensive. In Bonon, targets were deployed throughout the protected area, because G. p. palpalis was present everywhere, whereas in Chad and Uganda G. fuscipes fuscipes was found only the riverine fringing vegetation. Thus, differences between gHAT foci, in terms of tsetse ecology and human geography, impact on the cost-effectiveness of tsetse control. It also demonstrates the need to take into account both the area treated and protected alongside other impact indicators, such as the cost per person protected

Quantifying the burden of rhodesiense sleeping sickness in Urambo district, Tanzania

Sleeping sickness (human African trypanosomiasis - HAT) is a disease transmitted by tsetse flies and is always fatal if left untreated. The disease occurs in foci affecting poor communities with limited access to health service provision and as such the disease is often left undiagnosed, mistaken for more common afflictions. Even if diagnosed, sleeping sickness is costly to treat, both for health services and patients and their families in terms of costs of diagnosis, transport, hospital care, and the prolonged period of convalescence. Here we estimate the health burden of the acute form T. b. rhodesiense sleeping sickness in Urambo District, Tanzania in terms of Disability Adjusted Life Years (DALYs), the yardstick commonly used by policy makers to prioritize disease management practices, representing a year of healthy life lost to disease. In this single district, the burden of the disease over one year was estimated at 979 DALYs and the estimated monetary costs to health services for the 143 treated patients at US$11,841 and to the patients themselves at US$ 3,673 for direct medical costs and US$ 9,781 for indirect non-medical costs. Sleeping sickness thus places a considerable burden on the affected rural communities and health services

Restoration of self-awareness of hypoglycemia in adults with long-standing type 1 diabetes: hyperinsulinemic-hypoglycemic clamp substudy results from the HypoCOMPaSS trial.

OBJECTIVE: Impaired awareness of hypoglycemia (IAH) and defective counterregulation significantly increase severe hypoglycemia risk in type 1 diabetes (T1D). We evaluated restoration of IAH/defective counterregulation by a treatment strategy targeted at hypoglycemia avoidance in adults with T1D with IAH (Gold score ≥4) participating in the U.K.-based multicenter HypoCOMPaSS randomized controlled trial. RESEARCH DESIGN AND METHODS: Eighteen subjects with T1D and IAH (mean ± SD age 50 ± 9 years, T1D duration 35 ± 10 years, HbA1c 8.1 ± 1.0% [65 ± 10.9 mmol/mol]) underwent stepped hyperinsulinemic-hypoglycemic clamp studies before and after a 6-month intervention. The intervention comprised the HypoCOMPaSS education tool in all and randomized allocation, in a 2 × 2 factorial study design, to multiple daily insulin analog injections or continuous subcutaneous insulin infusion therapy and conventional glucose monitoring or real-time continuous glucose monitoring. Symptoms, cognitive function, and counterregulatory hormones were measured at each glucose plateau (5.0, 3.8, 3.4, 2.8, and 2.4 mmol/L), with each step lasting 40 min with subjects kept blinded to their actual glucose value throughout clamp studies. RESULTS: After intervention, glucose concentrations at which subjects first felt hypoglycemic increased (mean ± SE from 2.6 ± 0.1 to 3.1 ± 0.2 mmol/L, P = 0.02), and symptom and plasma metanephrine responses to hypoglycemia were higher (median area under curve for symptoms, 580 [interquartile range {IQR} 420-780] vs. 710 [460-1,260], P = 0.02; metanephrine, 2,412 [-3,026 to 7,279] vs. 5,180 [-771 to 11,513], P = 0.01). Glycemic threshold for deterioration of cognitive function measured by four-choice reaction time was unchanged, while the color-word Stroop test showed a degree of adaptation. CONCLUSIONS: Even in long-standing T1D, IAH and defective counterregulation may be improved by a clinical strategy aimed at hypoglycemia avoidance

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Study of elderly person's quality of life who reside in residential care in primary health care

<p><b>Note that column totals are independently modeled and may therefore not exactly match age-specific output summaries.</b></p