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View this table:
Table 1.

Characteristics of Studies, Subjects, and Diets

View this table:
Table 2.

Oils Used to Alter the Fat Composition of the Study Diets

The manipulation of fat composition in the various study diets was achieved primarily by the substitution of oils (eg, by adding the oils to various sauces, dressings, and baked items). This enabled the investigators to keep all other major components of the intervention diets comparable (ie, saturated fat, carbohydrate, fiber, dietary cholesterol). Most of the high-mono diets incorporated either olive or canola oil into the diets, whereas most of the high-poly diets used either sunflower, corn oil, or safflower oil. The oils used are summarized in Table 2 .

In the tables and figures, the results from individual studies are presented in order of decreasing proportion of kilocalories exchanged between monounsaturates and polyunsaturates on the high-mono versus the high-poly diet (eg, the study with the largest exchange, 25% kcal, Bonanome et al, 16 is listed first, at the top). Tables listing all of the serum lipid data from individual studies and all of the individual effect sizes and 95% CI generated from the lipid data are available in a technical report. 36

The 14 individual study effect sizes, the pooled effect sizes, and their 95% CI are presented in Fig 1 for the high-mono versus high-poly contrasts for LDL-C, HDL-C, and triglycerides. Statistically significant findings ( P <.05) are illustrated graphically by 95% CI that do not include zero. For LDL-C, 11 of the effect sizes were not significantly different from zero. One study shows a significant positive effect size (ie, LDL-C levels were higher on the high-mono diet), 14 whereas 2 of the studies show a significant negative effect size (ie, LDL-C levels were higher on the high-poly diet). 18 outlet real Diadora Exodus NYL 9 browse cheap online ejYt79MO2d
The individual study effect sizes for total-C in the high-mono versus high-poly dietary contrast are virtually identical to those for LDL-C (data not presented).

Figure 1.

Graphs show effect sizes (95% CI) of the 14 individual studies and the pooled effect size estimates for LDL-C, HDL-C, and triglycerides for the high-mono (Mono) vs high-poly (Poly) contrast. Pooled effect sizes represent the synthesis of the individual study effect sizes. The order of the studies from top to bottom in the Figure corresponds to the order of their presentation in Table 1 .

Similar to the findings for LDL-C, 10 of the 14 effect sizes for HDL-C were not statistically different from zero. Two studies show a significant positive effect size, Sam Edelman Womens Therese Slipon Loafer Pink Mauve fashion Style cheap online sneakernews cheap online clearance outlet locations manchester great sale cheap price cheap sale cheapest price iWLIocL
21 and 2 of the studies show a significant negative effect size. 26 28

Despite these attempts to risk-adjust based upon data that hospitals are already reporting to NHSN, there are limitations. For example, adjustment by test type accounts for only the pooled mean impact on rates resulting from differences in sensitivity between major test categories (eg, NAAT, toxin EIA) and does not account for differences in sensitivity between individual test manufacturers, nor potential interaction of C. difficile strain types on relative test sensitivity [ 22 , 23 ]. Similarly, there are inherent limitations in all surveillance adjusting for the disease risk in the surveyed population. For example, Thompson et al demonstrated how the Medicare Case Mix Index, a summary metric calculated at the hospital level and reflecting clinical complexity and resource consumption of patients within a hospital, could further explain variation across hospital CDI rates over and above the existing model [ 24 ]. However, any potential benefit to hospital performance improvement from additional risk adjustment strategies must be balanced by any increased data-reporting burden or impact on timeliness.

Clostridium difficile is the most commonly recognized cause of infectious diarrhea in healthcare settings. Among 711 acute care hospitals in 28 states conducting facility-wide inpatient LabID-CDI event reporting to NHSN in 2010, the pooled rate of HO-CDI was 7.4 (median, 5.4) per 10000 patient-days [ 25 ]. As these data were reported prior to development of the SIR, they were unadjusted; at that time, 35% of NHSN hospitals reported using NAATs. Based on data from the CDC’s Emerging Infections Program (EIP) [ 26 ] population-based surveillance system in 2011, the estimated number of incident CDI cases in the United States was 453000 (95% confidence interval [CI], 397100–508 500), with an incidence of 147.2 (95% CI, 129.1–165.3) cases/100000 persons [ 6 ]. The incidence was highest among those aged ≥65 years (627.7) and was greater among females and whites. Of the total estimated 453000 incident cases, 293300 (64.7%) were considered to be healthcare-associated, of which 37% were HO, 36% had their onset in long-term care facilities (LTCFs), and 28% were CO healthcare-associated (ie, specimen collected in an outpatient setting or ≤3 calendar days after hospital admission and documented overnight stay in a healthcare facility in the prior 12 weeks). Of the estimated 159700 community-associated CDI cases (ie, no documented overnight stay in a healthcare facility in the prior 12 weeks), 82% were associated with outpatient healthcare exposure; therefore, the overwhelming majority (94%) of all cases of CDI had a recent healthcare exposure [ 6 , 27 ].

A multistate prevalence survey of HAIs conducted by EIP in 2011 found that C. difficile was the most common causative pathogen, accounting for 61 of 504 (12.1%) HAIs identified in 183 hospitals [ 28 ]. The increasing burden of CDI was also noted in a network of community hospitals in the southeastern United States, where C. difficile surpassed methicillin-resistant Staphylococcus aureus (MRSA) as the most common cause of HAIs [ 29 ].

Figure 2
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Time interval between hospital admission and onset of infection for the most frequently isolated pathogens in a series of 24,179 cases of nosocomial bloodstream infection (BSI). CoNS, coagulase-negative staphylococci;

Figure 2
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Time interval between hospital admission and onset of infection for the most frequently isolated pathogens in a series of 24,179 cases of nosocomial bloodstream infection (BSI). CoNS, coagulase-negative staphylococci;

Primary BSI, in which no source could be determined, was seen in 12,893 patients (53%). Secondary BSI originated from intravenous catheters in 5749 patients (24%), from the urinary tract in 1580 patients (6.5%), and from the lower respiratory tract in 1539 patients (6%). However, culture samples from distant sites were infrequently collected; therefore, some points of origin might not have been detected.

Of the 1890 Candida isolates causing nosocomial BSI, C. albicans was the most common, accounting for 54% of cases of Candida BSI, followed in rank order by C. glabrata (19%), C. parapsilosis (11%), and C. tropicalis (11%) ( figure 3 ). Crude mortality was lowest for C. albicans infection (37%) and highest for C. krusei infection (59%) ( figure 3 ). There was an increase in the proportion of Candida species isolated from blood cultures from 8% in 1995 to 12% in 2002 ( P < .001, trend analysis). Also, the proportions of C. albicans and C. parapsilosis among these isolates increased between 1995 and 2002, but the proportions of C. tropicalis and C. glabrata decreased.

Figure 3
View large Download slide

Distribution of species in 1890 cases of bloodstream infection and associated crude mortality

Figure 3
View large Download slide

Distribution of species in 1890 cases of bloodstream infection and associated crude mortality

Antimicrobial susceptibility . Methicillin resistance was detected in 1699 S. aureus isolates (41% of tested isolates) and in 4946 CoNS isolates (75%). The proportion of S. aureus isolates with methicillin resistance was significantly higher among ICU patients than among ward patients (44% vs. 40%; P = .004), and there was also a trend toward a higher proportion of S. aureus isolates resistant to methicillin among patients without neutropenia than among patients with neutropenia (42% vs. 32%; P = .054, figure 4 ). The proportion of S. aureus isolates resistant to methicillin increased from 22% in 1995 to 57% in 2001 ( P < .001, trend analysis; figure 5 ).

Further Reading

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The worst-case scenario? If fossil fuel investment continues unchecked, policies to limit warming to 2°C are enacted, and a sell-off occurs, the global economy could stand to lose $4 trillion, the researchers write.

"For reference, the subprime mortgage market value loss that took place following the 2008 financial crisis was around US $0.25 trillion, leading to global stock market capitalization decline of about US $25 trillion," the paper notes.

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(a scenario that the researchers seem not to have considered). That might lessen the damage temporarily, but it's a stopgap measure for a country that exports fossil fuels to a changing world.

The researchers stress that this global fossil fuel contraction could happen with or without new climate policy, as low-carbon technology is adopted.

Instead, the researchers argue, the only way for a fuel-producing country to win is to lean into the transition—adopt climate policies that redirect investor dollars away from fossil fuel infrastructure and into renewable energy, and fewer dollars will be available to be stranded. "Further economic damage from a potential bubble burst could be avoided by decarbonizing early," the researchers advise.

They add that any one country, no matter how big it is, would likely not be able to turn the tide of global climate policy, so "an exposed country can mitigate the impact of stranding by divesting from fossil fuels as an insurance policy against what the rest of the world does."

The effects of this low-carbon transition are already becoming noticeable, even in the US, despite the Trump administration's pro-fossil fuel policies. Just last week, the Energy Information Agency (EIA) released data showing that in 2017, the electric power sector used less fossil fuel than it had in any year since 1994 .

In the absence of any US policy to temper the optimism of fossil fuel companies, some investors have demanded an accounting of how their investments are exposed to climate change policies and new low-carbon technologies. Exxon is the most notable: in May 2017, more than 60 percent of Exxon's investors voted in favor of a resolution that would require the company to compile an annual report on how global climate change policies could affect business. Though the resolution was non-binding, it showed that more and more investors are becoming concerned about the problem of stranded assets.

Just today, the UK's House of Commons Environmental Audit Committee issued a report recommending that large companies be required to report their climate change risk to shareholders. SP Global Platts wrote that, "If enacted into law, mandatory climate risk disclosure would likely impact the long-term availability of finance to support carbon-intensive assets and businesses."

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