IMPORTANCE To our knowledge, this is the first placebo-controlled randomized clinical trial to evaluate the efficacy of antidepressant pharmacotherapy, with and without complicated grief psychotherapy, in the treatment of complicated grief. OBJECTIVE To confirm the efficacy of a targeted complicated grief treatment (CGT), determine whether citalopram (CIT) enhances CGT outcome, and examine CIT efficacy without CGT. DESIGN, SETTING, AND PARTICIPANTS Included in the study were 395 bereaved adults who met criteria for CG recruited from March 2010 to September 2014 from academic medical centers in Boston, Massachusetts; New York, New York; Pittsburgh, Pennsylvania; and San Diego, California. Co-occurring substance abuse, psychosis, mania, and cognitive impairment were exclusionary. Study participants were randomized using site-specific permuted blocks stratified by major depression into groups prescribed CIT (n = 101), placebo (PLA; n = 99), CGT with CIT (n = 99), and CGT with PLA (n = 96). Independent evaluators conducted monthly assessments for 20 weeks. Response rates were compared under the intention-to-treat principle, including all randomized participants in a logistic regression with inverse probability weighting. INTERVENTIONS All participants received protocolized pharmacotherapy optimized by flexible dosing, psychoeducation, grief monitoring, and encouragement to engage in activities. Half were also randomized to receive manualized CGT in 16 concurrent weekly sessions. MAIN OUTCOMES AND MEASURES Complicated grief–anchored Clinical Global Impression scale measurments every 4 weeks. Response was measured as a rating of “much improved” or “very much improved.” RESULTS Of the 395 study participants, 308 (78.0%) were female and 325 (82.3%) were white. Participants’ response to CGT with PLA vs PLA (82.5% vs 54.8%; relative risk [RR], 1.51; 95% CI, 1.16–1.95; P = .002; number needed to treat [NNT], 3.6) suggested the efficacy of CGT, and the addition of CIT did not significantly improve CGT outcome (CGT with CIT vs CGT with PLA: 83.7% vs 82.5%; RR, 1.01; 95% CI, 0.88–1.17; P = .84; NNT, 84). However, depressive symptoms decreased significantly more when CIT was added to treatment (CGT with CIT vs CGT with PLA: model-based adjusted mean [standard error] difference, −2.06 [1.00]; 95% CI, −4.02 to −0.11; P = .04). By contrast, adding CGT improved CIT outcome (CIT vs CGT with CIT: 69.3% vs 83.7%; RR, 1.21; 95% CI, 1.00–1.46; P = .05; NNT, 6.9). Last, participant response to CIT was not significantly different from PLA at week 12 (45.9% vs 37.9%; RR, 1.21; 95% CI, 0.82–1.81; P = .35; NNT, 12.4) or at week 20 (69.3% vs 54.8%; RR, 1.26; 95% CI, 0.95–1.68; P = .11; NNT, 6.9). Rates of suicidal ideation diminished to a substantially greater extent among participants receiving CGT than among those who did not. CONCLUSIONS AND RELEVANCE Complicated grief treatment is the treatment of choice for CG, and the addition of CIT optimizes the treatment of co-occurring depressive symptoms. TRIAL REGISTRATION clinical...

IMPORTANCE Delirium is associated with increased hospital costs, health care complications, and increased mortality. Long-term consequences of delirium on cognition have not been synthesized and quantified via meta-analysis.OBJECTIVE To determine if an episode of delirium was an independent risk factor for long-term cognitive decline, and if it was, whether it was causative or an epiphenomenon in already compromised individuals.

Background The COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. Methods Beginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. Results Analysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. Conclusions This analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.

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Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.