Aggregation and Calibration of Agricultural Sector Models Through Crop Mix Restrictions and Marginal Profit Adjustments

All agricultural sector models must deal with aggregation and calibration somehow. The aggregation problem involves treating a group of producers as if they all responded in the same way as a single representative unit. The calibration problem concerns making a model reproduce as closely as possible an empirically observed set of decision maker actions. This paper shows how both calibration and aggregation are addressed through crop mix restrictions combined with marginal profit adjust-ments.mathematical programming, aggregation, calibration, crop mix, marginal cost, agricultural sector model, Agribusiness, C6, C61, Q1, Q11, Q17, Q18, R12, R13, R14,

Prebiotics and Inflammatory Bowel Disease

Inflammatory bowel disease risk factors include poor diet, and corresponding low intake of dietary fiber, specifically prebiotics, which is fermented by the gut microbiota. Dietary fibers, many of which are potential prebiotics, have hundreds to thousands of unique chemical structures that may promote bacteria or bacterial groups to provide beneficial health effects. In vitro and in vivo animal models provide some support for the use of prebiotics for inflammatory bowel disease through inflammation reduction. Studies using prebiotics in patients with inflammatory bowel disease are limited and focus on only a select few prebiotic substances. Keywords: Inflammatory bowel disease, Ulcerative colitis, Crohn disease, Prebiotics, Fibe

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

Scaling-up treatment of depression and anxiety: a global return on investment analysis

SummaryBackgroundDepression and anxiety disorders are highly prevalent and disabling disorders, which result not only in an enormous amount of human misery and lost health, but also lost economic output. Here we propose a global investment case for a scaled-up response to the public health and economic burden of depression and anxiety disorders.MethodsIn this global return on investment analysis, we used the mental health module of the OneHealth tool to calculate treatment costs and health outcomes in 36 countries between 2016 and 2030. We assumed a linear increase in treatment coverage. We factored in a modest improvement of 5% in both the ability to work and productivity at work as a result of treatment, subsequently mapped to the prevailing rates of labour participation and gross domestic product (GDP) per worker in each country.FindingsThe net present value of investment needed over the period 2016–30 to substantially scale up effective treatment coverage for depression and anxiety disorders is estimated to be US$147 billion. The expected returns to this investment are also substantial. In terms of health impact, scaled-up treatment leads to 43 million extra years of healthy life over the scale-up period. Placing an economic value on these healthy life-years produces a net present value of$310 billion. As well as these intrinsic benefits associated with improved health, scaled-up treatment of common mental disorders also leads to large economic productivity gains (a net present value of $230 billion for scaled-up depression treatment and$169 billion for anxiety disorders). Across country income groups, resulting benefit to cost ratios amount to 2·3–3·0 to 1 when economic benefits only are considered, and 3·3–5·7 to 1 when the value of health returns is also included.InterpretationReturn on investment analysis of the kind reported here can contribute strongly to a balanced investment case for enhanced action to address the large and growing burden of common mental disorders worldwide.FundingGrand Challenges Canada

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