Etiology and antimicrobial susceptibility of udder pathogens from cases of subclinical mastitis in dairy cows in Sweden

<p>Abstract</p> <p>Background</p> <p>A nationwide survey on the microbial etiology of cases of subclinical mastitis in dairy cows was carried out on dairy farms in Sweden. The aim was to investigate the microbial panorama and the occurrence of antimicrobial resistance. Moreover, differences between newly infected cows and chronically infected cows were investigated.</p> <p>Methods</p> <p>In total, 583 quarter milk samples were collected from 583 dairy cows at 226 dairy farms from February 2008 to February 2009. The quarter milk samples were bacteriological investigated and scored using the California Mastitis Test. Staphylococci were tested for betalactamase production and presence of resistance was evaluated in all specific udder pathogens. Differences between newly infected cows and chronically infected cows were statistically investigated using logistic regression analysis.</p> <p>Results</p> <p>The most common isolates of 590 bacteriological diagnoses were <it>Staphylococcus (S) aureus </it>(19%) and coagulase-negative staphylococci (CNS; 16%) followed by <it>Streptococcus (Str) dysgalactiae </it>(9%), <it>Str. uberis </it>(8%), <it>Escherichia (E.) coli </it>(2.9%), and <it>Streptococcus </it>spp. (1.9%). Samples with no growth or contamination constituted 22% and 18% of the diagnoses, respectively. The distribution of the most commonly isolated bacteria considering only bacteriological positive samples were: <it>S. aureus </it>- 31%, CNS - 27%, <it>Str. dysgalactiae </it>- 15%, <it>Str. uberis </it>- 14%, <it>E. coli </it>- 4.8%, and <it>Streptococcus </it>spp. - 3.1%. There was an increased risk of finding <it>S. aureus, Str. uberis </it>or <it>Str. dysgalactiae </it>in milk samples from chronically infected cows compared to findings in milk samples from newly infected cows. Four percent of the <it>S. aureus </it>isolates and 35% of the CNS isolates were resistant to penicillin G. Overall, resistance to other antimicrobials than penicillin G was uncommon.</p> <p>Conclusions</p> <p><it>Staphylococcus aureus </it>and CNS were the most frequently isolated pathogens and resistance to antimicrobials was rare.</p

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Designer policy for carbon and biodiversity co-benefits under global change

Carbon payments can help mitigate both climate change and biodiversity decline through the reforestation of agricultural land1. However, to achieve biodiversity co-benefits, carbon payments often require support from other policy mechanisms2 such as regulation3, 4, targeting5, 6, and complementary incentives7, 8. We evaluated 14 policy mechanisms for supplying carbon and biodiversity co-benefits through reforestation of carbon plantings (CP) and environmental plantings (EP) in Australia’s 85.3 Mha agricultural land under global change. The reference policy—uniform payments (bidders are paid the same price) with land-use competition (both CP and EP eligible for payments), targeting carbon—achieved significant carbon sequestration but negligible biodiversity co-benefits. Land-use regulation (only EP eligible) and two additional incentives complementing the reference policy (biodiversity premium, carbon levy) increased biodiversity co-benefits, but mostly inefficiently. Discriminatory payments (bidders are paid their bid price) with land-use competition were efficient, and with multifunctional targeting of both carbon and biodiversity co-benefits increased the biodiversity co-benefits almost 100-fold. Our findings were robust to uncertainty in global outlook, and to key agricultural productivity and land-use adoption assumptions. The results suggest clear policy directions, but careful mechanism design will be key to realising these efficiencies in practice. Choices remain for society about the amount of carbon and biodiversity co-benefits desired, and the price it is prepared to pay for them