Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Predicting the effect of habitat change on waterfowl communities: a novel empirical approach

Natural environmental changes, such as coastal erosion, and human developments, ranging from roads and marinas to global climate change, are leading to much habitat change in wetlands. It would be valuable to conservationists, governments and developers tob be able to predict the likely act of such evolution on the internationally important waterbird populationsin European wetlands. We present a method, based on relatively easily and cheaply determined environmental variables, which allows the effect of habitat Change on estuary wateifowl cornmunities to be predicted. The factors that best describe waterfowl communities are estuary length, channel and shore widths, exposure to swell, sediment type, longitude and latitude. The implications for waterfowl of any habitat change that affects these variables are discussed. It is suggested that when human developments are being designed they should take these factors into account in an attempt to minimise their impact on waterfowl

The abundance and distribution of waterfowl within Milford Haven after the Sea Empress oil spill year 1 report, September 1997

SIGLEAvailable from British Library Document Supply Centre-DSC:2354.730(182) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The abundance and distribution of waterfowl within Milford Haven after the Sea Empress oil spill, year 1 report

Also numbered as BTO research report no. 182Available from British Library Document Supply Centre-DSC:3096.2392(228) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Towards developing thresholds for waterbirds that take into account turnover

To attain international importance and thus protection as a Ramsar site or as a Special Protection Area (SPA) a wetland site must either “regularly” support at least 20,000 waterbirds or seabirds, or 1% of the individuals of a population of a species or subspecies of waterbird. In most cases, sites have been designated by using the maxima of individual counts. These counts will underestimate volume (i.e. total number) of birds passing through the site if turnover of birds occurs. Using count data, observations of individually marked birds and survival and recruitment mark-recapture models, we present three different methods (V1, V2 & V3) implemented in the StopOver Duration Analysis or SODA program (Choquet & Pradel 2007) for estimating the total volume of birds passing through a site. We use simulated data to determine their performance using both biased and unbiased data. Specifically, we tested whether the estimates of volume were biased where the following parameters varied: proportion of birds marked, daily resighting rate, timing of arrival, proportion of transients in the population, heterogeneity in the resighting rates (i.e. some individuals with a high or low resighting rate), variation in arrival and stopover time and count error. With a relatively simple dataset (single arrival, no biases), the proportion of individuals marked had little effect on the reliability of the resulting volume estimates for both V1 and V3. Estimates of volume from V2 were always overestimated. The major factor that caused a small positive bias in V1 and V3 was the resighting probability. Lower resighting probabilities caused a small positive bias in the volume estimates. Resighting heterogeneity (i.e. some birds more likely to be seen than others) caused a substantial positive bias for all estimators. Transience (i.e. some birds stopping over for shorter time than others) caused no bias in V1 and V3, but a strong negative bias in V2.Transience seemed to reduce the positive bias due to heterogeneity in V1 and V3 when both were present. The use of trap-dependent models (i.e. those that allow individuals to have differential recapture rates) showed some promise for V3 as little bias in the volume estimate was observed when there was a moderate amount of variation in individuals’ resighting rates. V1 & V3 performed well under scenarios of varying arrival and stopover duration as well as where error in the counts was introduced. V2 was consistently biased (see Table 4.1) The V3 method performed well and consistently had the highest precision; it is the method we recommend to use to estimate volume. It is important that goodness of fit tests are used to determine biases in the data and appropriate models are used in Program SODA. Although some biases in the data have little effect on the resulting volume estimates, care must be taken when setting up a study to reduce bias. We present eight different ways of ensuring that bias is reduced during the collection of data. Practical ways to deal with biases are discussed. Recommendations (see section 4.2 for further details) are to: (i) Count at the same time as reading colour rings; (ii) Count at approximately one-third of the length of stay interval, e.g. if the species is thought to stay ten days on a site during passage then count every 5 days; (iii) aim to resight > 30 individuals during every count period, although preferably more; obtain as far as is possible representative samples of the population being studied; (iv) the timing of marking of the study species, the number of sites included, and the timing of counts is discussed