12 research outputs found

    Reducing the environmental impact of trials: a comparison of the carbon footprint of the CRASH-1 and CRASH-2 clinical trials

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    BACKGROUND: All sectors of the economy, including the health research sector, must reduce their carbon emissions. The UK National Institute for Health Research has recently prepared guidelines on how to minimize the carbon footprint of research. We compare the carbon emissions from two international clinical trials in order to identify where emissions reductions can be made. METHODS: We conducted a carbon audit of two clinical trials (the CRASH-1 and CRASH-2 trials), quantifying the carbon dioxide emissions produced over a one-year audit period. Carbon emissions arising from the coordination centre, freight delivery, trial-related travel and commuting were calculated and compared. RESULTS: The total emissions in carbon dioxide equivalents during the one-year audit period were 181.3 tonnes for CRASH-1 and 108.2 tonnes for CRASH-2. In total, CRASH-1 emitted 924.6 tonnes of carbon dioxide equivalents compared with 508.5 tonnes for CRASH-2. The CRASH-1 trial recruited 10,008 patients over 5.1 years, corresponding to 92 kg of carbon dioxide per randomized patient. The CRASH-2 trial recruited 20,211 patients over 4.7 years, corresponding to 25 kg of carbon dioxide per randomized patient. The largest contributor to emissions in CRASH-1 was freight delivery of trial materials (86.0 tonnes, 48% of total emissions), whereas the largest contributor in CRASH-2 was energy use by the trial coordination centre (54.6 tonnes, 30% of total emissions). CONCLUSIONS: Faster patient recruitment in the CRASH-2 trial largely accounted for its greatly increased carbon efficiency in terms of emissions per randomized patient. Lighter trial materials and web-based data entry also contributed to the overall lower carbon emissions in CRASH-2 as compared to CRASH-1. TRIAL REGISTRATION NUMBERS: CRASH-1: ISRCTN74459797CRASH-2: ISRCTN86750102

    Use of the revised World Health Organization cluster survey methodology to classify measles-rubella vaccination campaign coverage in 47 counties in Kenya, 2016

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    <div><p>Introduction</p><p>To achieve measles elimination, two doses of measles-containing vaccine (MCV) are provided through routine immunization services or vaccination campaigns. In May 2016, Kenya conducted a measles-rubella (MR) vaccination campaign targeting 19 million children aged 9 months–14 years, with a goal of achieving ≄95% coverage. We conducted a post-campaign cluster survey to estimate national coverage and classify coverage in Kenya’s 47 counties.</p><p>Methods</p><p>The stratified multi-stage cluster survey included data from 20,011 children in 8,253 households sampled using the recently revised World Health Organization coverage survey methodology (2015). Point estimates and 95% confidence intervals (95% CI) of national campaign coverage were calculated, accounting for study design. County vaccination coverage was classified as ‘pass,’ ‘fail,’ or ‘intermediate,’ using one-sided hypothesis tests against a 95% threshold.</p><p>Results</p><p>Estimated national MR campaign coverage was 95% (95% CI: 94%-96%). Coverage differed significantly (p < 0.05) by child’s school attendance, mother’s education, household wealth, and other factors. In classifying coverage, 20 counties passed (≄95%), two failed (<95%), and 25 were intermediate (unable to classify either way). Reported campaign awareness among caretakers was 92%. After the 2016 MR campaign, an estimated 93% (95% CI: 92%–94%) of children aged 9 months to 14 years had received ≄2 MCV doses; 6% (95% CI: 6%–7%) had 1 MCV dose; and 0.7% (95% CI: 0.6%–0.9%) remained unvaccinated.</p><p>Conclusions</p><p>Kenya reached the MR campaign target of 95% vaccination coverage, representing a substantial achievement towards increasing population immunity. High campaign awareness reflected the comprehensive social mobilization strategy implemented in Kenya and supports the importance of including strong communications platforms in future vaccination campaigns. In counties with sub-optimal MR campaign coverage, further efforts are needed to increase MCV coverage to achieve the national goal of measles elimination by 2020.</p></div

    Classification of measles-rubella campaign vaccination coverage by county—Kenya, 2016.

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    <p>Coverage point estimates (one-sided upper and lower 95% confidence bounds [1-S 95% CB]) for children aged 9 months–14 years by country are graphed and printed in the columns to the right of the graph, along with the design effects (DE), intra-cluster correlation coefficients (ICC), and effective sample sizes (ESS = observed sample size / DE; where DE <1.0, a DE of 1.0 was used to calculate ESS). County coverages colored in green were classified as ‘passing,’ or likely to have coverage ≄95% (i.e., lower confidence bound was >95%). Coverages depicted in yellow were classified as ‘intermediate,’ or unable to confidently classify as above or below 95% given the survey sample size (i.e., upper and lower confidence bounds straddled the 95% threshold). Coverages shown in red were classified as ‘failing,’ or likely to have coverage <95% (i.e., upper confidence bound was below 95%).</p

    Estimated coverage with two doses of measles-containing vaccine (MCV) after the 2016 measles-rubella (MR) campaign in Kenya.

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    <p>(A) Graph of the number of MCV doses received by one-year age cohort, including the MCV1 and MCV2 doses provided by routine immunization services, the MR campaign dose, and previous measles campaign doses as documented by vaccination cards, campaign finger-markings and caregivers’ recall. (B) Map of two-dose MCV coverage of children aged 9 months–14 years by county.</p
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