The Lantern Vol. 60, No. 2, Summer 1993

• Wake • Misconception • Cliche • Standard Oil • Lake Effect • Charlotte • Psychedelic Iridescent Infidelity • A Playground in Winter • Shooting Pool with Angels • The Blood Through Our Veins • Iced Coffee • Buzz Kill • Immortality • Cathodic Union • Crush • Mushrooms • Conversing • Eggplant • A Letter to the Civil Rights Movement • Still Sitting, Contemplating • Sensible Love • Monsters Under the Bed • Poison Rock • Waiting at the Dentist • Fate • Static • The Three C\u27s • As We Frolic • Nest • A Bottle of Wine and Patsy Cline • Bottoms of Pages, Backs of Bookshttps://digitalcommons.ursinus.edu/lantern/1143/thumbnail.jp

Treatment effect modification due to comorbidity : Individual participant data meta-analyses of 120 randomised controlled trials

BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity

Validation of HNO3, ClONO2, and N2O5 from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS)

The Atmospheric Chemistry Experiment (ACE) satellite was launched on 12 August 2003. Its two instruments measure vertical profiles of over 30 atmospheric trace gases by analyzing solar occultation spectra in the ultraviolet/visible and infrared wavelength regions. The reservoir gases HNO3, ClONO2, and N2O5 are three of the key species provided by the primary instrument, the ACE Fourier Transform Spectrometer (ACE-FTS). This paper describes the ACE-FTS version 2.2 data products, including the N2O5 update, for the three species and presents validation comparisons with available observations. We have compared volume mixing ratio (VMR) profiles of HNO3, ClONO2, and N2O5 with measurements by other satellite instruments (SMR, MLS, MIPAS), aircraft measurements (ASUR), and single balloon-flights (SPIRALE, FIRS-2). Partial columns of HNO3 and ClONO2 were also compared with measurements by ground-based Fourier Transform Infrared (FTIR) spectrometers. Overall the quality of the ACE-FTS v2.2 HNO3 VMR profiles is good from 18 to 35 km. For the statistical satellite comparisons, the mean absolute differences are generally within ±1 ppbv ±20%) from 18 to 35 km. For MIPAS and MLS comparisons only, mean relative differences lie within±10% between 10 and 36 km. ACE-FTS HNO3 partial columns (~15–30 km) show a slight negative bias of −1.3% relative to the ground-based FTIRs at latitudes ranging from 77.8° S–76.5° N. Good agreement between ACE-FTS ClONO2 and MIPAS, using the Institut für Meteorologie und Klimaforschung and Instituto de Astrofísica de Andalucía (IMK-IAA) data processor is seen. Mean absolute differences are typically within ±0.01 ppbv between 16 and 27 km and less than +0.09 ppbv between 27 and 34 km. The ClONO2 partial column comparisons show varying degrees of agreement, depending on the location and the quality of the FTIR measurements. Good agreement was found for the comparisons with the midlatitude Jungfraujoch partial columns for which the mean relative difference is 4.7%. ACE-FTS N2O5 has a low bias relative to MIPAS IMK-IAA, reaching −0.25 ppbv at the altitude of the N2O5 maximum (around 30 km). Mean absolute differences at lower altitudes (16–27 km) are typically −0.05 ppbv for MIPAS nighttime and ±0.02 ppbv for MIPAS daytime measurements

PS Resource Assessment of Oil and Gas Plays in Paleozoic Basins of Eastern Canada*

There are three major Paleozoic basins in eastern Canada: � Cambrian-Ordovician St. Lawrence shallow marine platform and coeval deep water facies � Silurian-Devonian shallow to deep marine Gaspé Belt � Devonian-Permian terrestrial to shallow marine Maritimes Basin The sedimentary successions are bounded by tectonically-generated unconformities- the Taconian unconformity separating Cambrian-Ordovician from Silurian-Devonian strata and the Acadian unconformity at the base of the late Devonian-Permian strata. Each basin contains unique source rock and reservoir units and specific trap types. All of the basins contain producing or discovered hydrocarbon fields but there has been no independent evaluation of their oil and gas resource potential. Over the past five years the Geological Survey of Canada and its partners have acquired new hydrocarbon systems data, in preparation for a first regional hydrocarbon play assessment of Paleozoic strata in eastern Canada. A total of 16 conventional and 2 unconventional plays have been identified. Seven conventional plays are recognized in Cambrian-Ordovician strata: � Cambrian rift sandstones � Lower Ordovician hydrothermal dolomite (HTD) � carbonate thrust slices at the Appalachian structural fron

Ozone tropospheric and stratospheric trends (1995-2012) at six ground-based FTIR stations (28°N to 79°N)

In the frame of the Network for the Detection of Atmospheric Composition Change (NDACC), contributing ground-based stations have joined their efforts to homogenize and optimize the retrievals of ozone profiles from FTIR (Fourier transform infrared) solar absorption spectra. Using the optimal estimation method, distinct vertical information can be obtained in four layers: ground-10 km, 10-18 km, 18-27 km, and 27-42 km, in addition to total column amounts. In a previous study, Vigouroux et al. (2008) applied a bootstrap resampling method to determine the trends of the ozone total and four partial columns, over the period 1995-2004 at Western European stations. The updated trends for the period 1995-2009 have been published in the WMO 2010 report. Here, we present the updated trends and their uncertainties, for the 1995-2012 period, for the different altitude ranges, above five European stations (28°N-79°N) and above the station Thule, Greenland (77°N). In this work, the trends have been estimated using a multiple regression model including some explanatory variables responsible for the ozone variability, such as the Quasi Biennial Oscillation (QBO), the solar flux, the Arctic Oscillation (AO) or El Niño-Southern Oscillation (ENSO). A major result is the significant positive trend of ozone in the upper stratosphere, observed at the Jungfraujoch (47°N), which is a typical mid-latitude site, as well as at the high latitude stations. This positive trend in the upper stratosphere at Jungfraujoch provides a sign of ozone recovery at mid-latitudes

Validation of ACE-FTS v2.2 methane profiles from the upper troposphere to the lower mesosphere

The ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) solar occultation instrument that was launched onboard the Canadian SCISAT-1 satellite in August 2003 is measuring vertical profiles from the upper troposphere to the lower mesosphere for a large number of atmospheric constituents. Methane is one of the key species. The version v2.2 data of the ACE-FTS CH4 data have been compared to correlative satellite, balloon-borne and ground-based Fourier transform infrared remote sensing data to assess their quality. The comparison results indicate that the accuracy of the data is within 10% in the upper troposphere – lower stratosphere, and within 25% in the middle and higher stratosphere up to the lower mesosphere (<60 km). The observed differences are generally consistent with reported systematic uncertainties. ACE-FTS is also shown to reproduce the variability of methane in the stratosphere and lower mesosphere

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials

Background People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). Methods and findings We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity–treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes—interaction term for comorbidity count 0.004, 95% CI −0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma—interaction term −0.22, 95% CI −1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. Conclusions Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity. Peter Hanlon and colleagues conduct meta-analyses of individual participant data extracted from 120 randomised controlled trials to investigate whether comorbidity modifies treatment effects. Author summary Why was this study done? There is often uncertainty about how treatments for single conditions should be applied to people with 2 or more long-term conditions (multimorbidity). People with multimorbidity are underrepresented in randomised controlled trials (RCTs); however, trials rarely report whether the efficacy of treatment differs by the number of additional long-term conditions (comorbidities) or in the presence of specific comorbidities. What did the researchers do and find? We analysed individual-participant data from 120 RCTs including 128,331 participants across 23 index conditions to assess whether the efficacy of treatment differed depending on the number of comorbidities or in the presence of any of the most common comorbidities. We found no evidence that treatment efficacy differed depending on the number of comorbidities, or by any specific comorbidities, for any of the index conditions and treatment comparisons included in this analysis. What do these findings mean? Within the range of comorbidities included within these trials, treatment effects did not vary by comorbidity. These findings can be used within evidence syntheses to estimate the likely overall benefit of treatments in the context of multimorbidity. These findings are limited by the fact that people with multiple comorbidities are underrepresented in trials, and those with the highest degree of comorbidity are often excluded

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis

BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202)

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.

BackgroundHealth-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life.Methods and findingsIncluded trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631.ConclusionsTreatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity.Trial registrationA prespecified protocol was registered on PROSPERO (CRD42018048202)

Validation of ACE-FTS v2.2 measurements of HCl, HF, CCl3F and CCl2F2 using space-, balloon- and ground-based instrument observations

Hydrogen chloride (HCl) and hydrogen fluoride (HF) are respectively the main chlorine and fluorine reservoirs in the Earth's stratosphere. Their buildup resulted from the intensive use of man-made halogenated source gases, in particular CFC-11 (CCl3F) and CFC-12 (CCl2F2), during the second half of the 20th century. It is important to continue monitoring the evolution of these source gases and reservoirs, in support of the Montreal Protocol and also indirectly of the Kyoto Protocol. The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) is a space-based instrument that has been performing regular solar occultation measurements of over 30 atmospheric gases since early 2004. In this validation paper, the HCl, HF, CFC-11 and CFC-12 version 2.2 profile data products retrieved from ACE-FTS measurements are evaluated. Volume mixing ratio profiles have been compared to observations made from space by MLS and HALOE, and from stratospheric balloons by SPIRALE, FIRS-2 and Mark-IV. Partial columns derived from the ACE-FTS data were also compared to column measurements from ground-based Fourier transform instruments operated at 12 sites. ACE-FTS data recorded from March 2004 to August 2007 have been used for the comparisons. These data are representative of a variety of atmospheric and chemical situations, with sounded air masses extending from the winter vortex to summer sub-tropical conditions. Typically, the ACE-FTS products are available in the 10-50 km altitude range for HCl and HF, and in the 7-20 and 7-25 km ranges for CFC-11 and -12, respectively. For both reservoirs, comparison results indicate an agreement generally better than 5-10% above 20 km altitude, when accounting for the known offset affecting HALOE measurements of HCl and HF. Larger positive differences are however found for comparisons with single profiles from FIRS-2 and SPIRALE. For CFCs, the few coincident measurements available suggest that the differences probably remain within +/-20%