Hyperketonaemia risk lower in organic cows housed in free stalls

The variation in the incidence of hyperketonaemia is marked between individual herds, and even though organic farms have some feeding related factors predisposing to hyperketonaemia, there are also some management practices (especially in loose housing systems) which might act as preventive factors. However, it may be advisable for organic farmers to favour moderate milk production when selecting cows for a herd

Data-driven analyses of watersheds as coupled human-nature systems

Both climate and human activities alter watershed characteristics. Because climate will continue to change and human population will continue to increase, we can expect that all watersheds will change during the foreseeable future. Interactions between climate change and human activity drive non-stationary trends in the hydrologic cycle, which make watersheds more difficult to manage. In the 21st century, successful management of water resources requires an improved understanding of the emergent properties of coupled human and natural systems and fully integrated land and water management. The primary goal of this research is to better understand the emergent properties of watersheds as coupled human-nature systems, namely streamflow frequency and instream biotic integrity. We also aim to understand how the dominant controls of these emergent properties change through time. This study targets these needs by quantitatively linking natural and anthropogenic drainage area characteristics to flow frequency and measures of ecological health using data mining techniques. The fact that drainage area characteristics for watersheds in the U.S. are known (in the case of ungauged watersheds) or readily predicted (in the case of future conditions) makes them ideal independent variables for predicting watershed response. The basic idea is to define quantitative indices (e.g., flow duration curve, index of biotic integrity) as a function of known drainage area characteristics using data mining algorithms. A novel algorithm called Model Tree Ensembles was developed and tested as a way to predict flow duration curves in ungaged, human-impacted basins. The importance of environmental factors to fish biotic metrics was assessed using conventional measures of variable importance as well as more advanced information theory-based techniques. Understanding the relative influence of natural and man-made drainage area characteristics on streamflow and fisheries is essential for sustainable management of regional water resources (i.e., water management) and watershed protection (i.e., land management). A lack of understanding of the combined effects and relative importance of major components of the hydrologic cycle hinders sustainable management of water supplies. In summary, this study determined the relative importance of natural and anthropogenic drainage area characteristics to emergent properties of coupled human-nature systems, namely streamflow frequency and instream biotic integrity, as well as how those variables change through time. The ability of these models to quantitatively assess a wide range of possible causes of streamflow and biotic community variability is evaluated. The concepts and methods are generalized and can be applied to other watersheds and response variables

The Impact of CMS CoP on Kidney Transplant Waiting Times

SRTR program reports provide detailed information on transplant center performance relative to risk-adjusted expected values. Designed to improve outcomes, the behavioral implications of these reports may generate a longer wait time for transplant. UNOS data for 28,839 deceased donor kidney transplants performed during 6/2007- 6/2010 and 79,725 registered patients waiting for a kidney transplant during this time period were merged with SRTR program report data; Patient-specific and transplant center controls were created. An indicator variable was constructed for whether or not a transplant center did not meet the Centers for Medicare and Medicaid Services (CMS) Conditions of Participation (CoP) during a patient’s waiting period for a transplant. A censored Cox-proportional hazard model was utilized to investigate the impact of CMS CoP on the length of time until transplant. Data analysis reveals that a transplant center’s failure to meet either the 1-year graft or patient survival rates, according to CMS criteria, is associated with the expected waiting time until transplantation. Further the results suggest that centers may elect to transplant healthier patients and patients for whom they would receive a risk compensation in the SRTR model

Data integration - the foundation stone for measuring portfolio development impact

International donors and investors need to monitor the progress of their individual investments and the alignment with their internal strategies in order to show their impact. To demonstrate overall impact, they need to have a harmonized portfolio level view of those individual investments in order to support future decision making. Through the monitoring work with our donor, we have developed Theories of Change and Action models to derive appropriate metrics to measure their portfolio impact. However, this requires integrating and collating data from multiple implementing partners (grantees) to harmonize to a set of standard metrics. Working with different grantees, each with their own individual data systems, creates challenges, often the biggest being people and processes. We have developed processes to facilitate interoperability and integration of disparate data sets, part of working towards FAIR data (findability, accessibility, interoperability, and reusability). We have written code based on an adaptor design pattern to convert grantee data to a standard portfolio format and harmonized grantee data to standard terms (e.g. livestock disease names). Through close partnerships with grantees, we have explained the significance of this work and worked together to map data to a standard portfolio format. Instead of requiring a common portfolio format, grantees submit data in a format that is specific to their organisation. This reduces the risk of data errors and saves grantees time in data preparation. As such, this is an equitable process that supports those grantees not having in-house data capacity and ultimately produces higher quality metrics. Interactive dashboards are used to disseminate the data which can be disaggregated by country, or domain specific terms. Metrics are based on both measured and modelled results, modelling is used to populate metrics where it is either not cost-effective or practical to gather specific field data or if impact is expected to occur in the future, so requires foresight and prediction. For example, we model the net economic impact of specific animal vaccines or therapeutics used by small-scale livestock producers. Gaps in available data to parameterise models are filled by literature and/or elicitation of expert opinion. Uncertainty in the data populating the metrics is communicated through a color-coded scheme. Clearly, the developmental and scientific rationale for the data collection and analytics are fundamental for an understanding of the socio-economic context of the portfolio, for which data integration lays the foundation

Patterns of mortality in domesticated ruminants in Ethiopia

BACKGROUND: Premature death of livestock is a problem in all ruminant production systems. While the number of premature ruminant deaths in a country is a reasonable indicator for the nation's health, few data sources exist in a country like Ethiopia that can be used to generate valid estimates. The present study aimed to establish if three different data sets, each with imperfect information on ruminant mortality, including abortions, could be combined into improved estimates of nationwide mortality in Ethiopia. METHODS: We combined information from a recent survey of ruminant mortality with information from the Living Standards Measurement Study and the Disease Outbreak and Vaccination Reporting dataset. Generalized linear mixed and hurdle models were used for data analysis, with results summarized using predicted outcomes. RESULTS: Analyses indicated that most herds experienced zero mortality and reproductive losses, with rare occasions of larger losses. Diseases causing deaths varied greatly both geographically and over time. There was little agreement between the different datasets. While the models aid the understanding of patterns of mortality and reproductive losses, the degree of variation observed limited the predictive scope. CONCLUSIONS: The models revealed some insight into why mortality rates are variable over time and are therefore less useful in measuring production or health status, and it is suggested that alternative measures of productivity, such as number of offspring raised to 1 year old per dam, would be more stable over time and likely more indicative

Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data.

Prospective, population-based studies that recruit participants in mid-life are valuable resources for dementia research. Follow-up in these studies is often through linkage to routinely-collected healthcare datasets. We investigated the accuracy of these datasets for dementia case ascertainment in a validation study using data from UK Biobank-an open access, population-based study of > 500,000 adults aged 40-69 years at recruitment in 2006-2010. From 17,198 UK Biobank participants recruited in Edinburgh, we identified those with ≥ 1 dementia code in their linked primary care, hospital admissions or mortality data and compared their coded diagnoses to clinical expert adjudication of their full-text medical record. We calculated the positive predictive value (PPV, the proportion of cases identified that were true positives) for all-cause dementia, Alzheimer's disease and vascular dementia for each dataset alone and in combination, and explored algorithmic code combinations to improve PPV. Among 120 participants, PPVs for all-cause dementia were 86.8%, 87.3% and 80.0% for primary care, hospital admissions and mortality data respectively and 82.5% across all datasets. We identified three algorithms that balanced a high PPV with reasonable case ascertainment. For Alzheimer's disease, PPVs were 74.1% for primary care, 68.2% for hospital admissions, 50.0% for mortality data and 71.4% in combination. PPV for vascular dementia was 43.8% across all sources. UK routinely-collected healthcare data can be used to identify all-cause dementia in prospective studies. PPVs for Alzheimer's disease and vascular dementia are lower. Further research is required to explore the geographic generalisability of these findings

The diagnosis, burden and prognosis of dementia: a record-linkage cohort study in England

Objectives: Electronic health records (EHR) might be a useful resource to study the risk factors and clinical care of people with dementia. We sought to determine the diagnostic validity of dementia captured in linked EHR. Methods and findings: A cohort of adults in linked primary care, hospital, disease registry and mortality records in England, [CALIBER (CArdiovascular disease research using LInked Bespoke studies and Electronic health Records)]. The proportion of individuals with dementia, Alzheimer’s disease, vascular and rare dementia in each data source was determined. A comparison was made of symptoms and care between people with dementia and age-, sex- and general practice-matched controls, using conditional logistic regression. The lifetime risk and prevalence of dementia and mortality rates in people with and without dementia were estimated with random-effects Poisson models. There were 47,386 people with dementia: 12,633 with Alzheimer’s disease, 9540 with vascular and 1539 with rare dementia. Seventy-four percent of cases had corroborating evidence of dementia. People with dementia were more likely to live in a deprived area (conditional OR 1.26;95%CI:1.20–1.31 most vs least deprived), have documented memory impairment (cOR = 11.97;95%CI:11.24–12.75), falls (cOR = 2.36;95%CI:2.31–2.41), depression (cOR = 2.03; 95%CI:1.98–2.09) or anxiety (cOR = 1.27; 95%CI:1.23–1.32). The lifetime risk of dementia at age 65 was 9.2% (95%CI:9.0%-9.4%), in men and 14.9% (95%CI:14.7%-15.1%) in women. The population prevalence of recorded dementia increased from 0.3% in 2000 to 0.7% in 2010. A higher mortality rate was observed in people with than without dementia (IRR = 1.56;95%CI:1.54–1.58). Conclusions: Most people with a record of dementia in linked UK EHR had some corroborating evidence for diagnosis. The estimated 10-year risk of dementia was higher than published population-based estimations. EHR are therefore a promising source of data for dementia research

Correction: The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England

[This corrects the article DOI: 10.1371/journal.pone.0199026.]