Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials: a meta-epidemiological study.

BACKGROUND: Researchers and decision-makers often use evidence from randomised controlled trials (RCTs) to determine the efficacy or effectiveness of a treatment or intervention. Studies with observational designs are often used to measure the effectiveness of an intervention in 'real world' scenarios. Numerous study designs and their modifications (including both randomised and observational designs) are used for comparative effectiveness research in an attempt to give an unbiased estimate of whether one treatment is more effective or safer than another for a particular population. An up-to-date systematic analysis is needed to identify differences in effect estimates from RCTs and observational studies. This updated review summarises the results of methodological reviews that compared the effect estimates of observational studies with RCTs from evidence syntheses that addressed the same health research question. OBJECTIVES: To assess and compare synthesised effect estimates by study type, contrasting RCTs with observational studies. To explore factors that might explain differences in synthesised effect estimates from RCTs versus observational studies (e.g. heterogeneity, type of observational study design, type of intervention, and use of propensity score adjustment). To identify gaps in the existing research comparing effect estimates across different study types. SEARCH METHODS: We searched MEDLINE, the Cochrane Database of Systematic Reviews, Web of Science databases, and Epistemonikos to May 2022. We checked references, conducted citation searches, and contacted review authors to identify additional reviews. SELECTION CRITERIA: We included systematic methodological reviews that compared quantitative effect estimates measuring the efficacy or effectiveness of interventions tested in RCTs versus in observational studies. The included reviews compared RCTs to observational studies (including retrospective and prospective cohort, case-control and cross-sectional designs). Reviews were not eligible if they compared RCTs with studies that had used some form of concurrent allocation. DATA COLLECTION AND ANALYSIS: Using results from observational studies as the reference group, we examined the relative summary effect estimates (risk ratios (RRs), odds ratios (ORs), hazard ratios (HRs), mean differences (MDs), and standardised mean differences (SMDs)) to evaluate whether there was a relatively larger or smaller effect in the ratio of odds ratios (ROR) or ratio of risk ratios (RRR), ratio of hazard ratios (RHR), and difference in (standardised) mean differences (D(S)MD). If an included review did not provide an estimate comparing results from RCTs with observational studies, we generated one by pooling the estimates for observational studies and RCTs, respectively. Across all reviews, we synthesised these ratios to produce a pooled ratio of ratios comparing effect estimates from RCTs with those from observational studies. In overviews of reviews, we estimated the ROR or RRR for each overview using observational studies as the reference category. We appraised the risk of bias in the included reviews (using nine criteria in total). To receive an overall low risk of bias rating, an included review needed: explicit criteria for study selection, a complete sample of studies, and to have controlled for study methodological differences and study heterogeneity. We assessed reviews/overviews not meeting these four criteria as having an overall high risk of bias. We assessed the certainty of the evidence, consisting of multiple evidence syntheses, with the GRADE approach. MAIN RESULTS: We included 39 systematic reviews and eight overviews of reviews, for a total of 47. Thirty-four of these contributed data to our primary analysis. Based on the available data, we found that the reviews/overviews included 2869 RCTs involving 3,882,115 participants, and 3924 observational studies with 19,499,970 participants. We rated 11 reviews/overviews as having an overall low risk of bias, and 36 as having an unclear or high risk of bias. Our main concerns with the included reviews/overviews were that some did not assess the quality of their included studies, and some failed to account appropriately for differences between study designs - for example, they conducted aggregate analyses of all observational studies rather than separate analyses of cohort and case-control studies. When pooling RORs and RRRs, the ratio of ratios indicated no difference or a very small difference between the effect estimates from RCTs versus from observational studies (ratio of ratios 1.08, 95% confidence interval (CI) 1.01 to 1.15). We rated the certainty of the evidence as low. Twenty-three of 34 reviews reported effect estimates of RCTs and observational studies that were on average in agreement. In a number of subgroup analyses, small differences in the effect estimates were detected: - pharmaceutical interventions only (ratio of ratios 1.12, 95% CI 1.04 to 1.21); - RCTs and observational studies with substantial or high heterogeneity; that is, I2 ≥ 50% (ratio of ratios 1.11, 95% CI 1.04 to 1.18); - no use (ratio of ratios 1.07, 95% CI 1.03 to 1.11) or unclear use (ratio of ratios 1.13, 95% CI 1.03 to 1.25) of propensity score adjustment in observational studies; and - observational studies without further specification of the study design (ratio of ratios 1.06, 95% CI 0.96 to 1.18). We detected no clear difference in other subgroup analyses. AUTHORS' CONCLUSIONS: We found no difference or a very small difference between effect estimates from RCTs and observational studies. These findings are largely consistent with findings from recently published research. Factors other than study design need to be considered when exploring reasons for a lack of agreement between results of RCTs and observational studies, such as differences in the population, intervention, comparator, and outcomes investigated in the respective studies. Our results underscore that it is important for review authors to consider not only study design, but the level of heterogeneity in meta-analyses of RCTs or observational studies. A better understanding is needed of how these factors might yield estimates reflective of true effectiveness.

Acceptability and feasibility of tuberculosis and diabetes mellitus bidirectional screening and joint treatment services in Malawi: a cross-sectional study and a policy document review.

OBJECTIVES: A cross-sectional and a policy document review study was performed to investigate perceived acceptability and feasibility to implementing different integration measures for tuberculosis (TB) and diabetes mellitus (DM) healthcare among healthcare workers (HCWs) and health managers, and to describe policy influence through a policy documents review in Malawi. SETTING: The survey was performed at eight hospitals, ministry of health offices and 10 non-governmental organisations. We collected data in March and April 2021. PARTICIPANTS: Of 95 HCWs and health managers invited; 92 participated. 21/92 (23%) were female, and 17/92 (18%) participants were from clinics that piloted the integrated care for TB and DM. OUTCOME MEASURES: We described awareness levels on TB/DM comorbidity, perceptions and experiences in TB/DM care. Furthermore, development processes and contents of included documents were analysed. RESULTS: 16/17 (94%) of HCWs from clinics piloting integrated care and 65/75 (86%) HCWs from hospitals that do not use integrated care for TB and DM responded that integrated care was acceptable and feasible. In qualitative data, shortage of resources, inadequate information sharing were common themes. We included seven relevant documents for the analysis. On development process and content, six of seven documents were scored ≥70%. In these documents, DM is a recognised risk factor for TB, and integration of healthcare services for infectious diseases and non-communicable diseases is recommended, however, these documents lacked information specifically on integrated care for TB and DM. CONCLUSION: In this study, we identified inadequate information sharing, and lack of resources as major factors impeding implementation of integration of services, however, awareness on TB/DM comorbidity was high.

Fluvoxamine for the treatment of COVID-19.

BACKGROUND: Fluvoxamine is a selective serotonin reuptake inhibitor (SSRI) that has been approved for the treatment of depression, obsessive-compulsive disorder, and a variety of anxiety disorders; it is available as an oral preparation. Fluvoxamine has not been approved for the treatment of infections, but has been used in the early treatment of people with mild to moderate COVID-19. As there are only a few effective therapies for people with COVID-19 in the community, a thorough understanding of the current evidence regarding the efficacy and safety of fluvoxamine as an anti-inflammatory and possible anti-viral treatment for COVID-19, based on randomised controlled trials (RCTs), is needed. OBJECTIVES: To assess the efficacy and safety of fluvoxamine in addition to standard care, compared to standard care (alone or with placebo), or any other active pharmacological comparator with proven efficacy for the treatment of COVID-19 outpatients and inpatients. SEARCH METHODS: We searched the Cochrane COVID-19 Study Register (including Cochrane Central Register of Controlled Trials, MEDLINE, Embase, ClinicalTrials.gov, WHO ICTRP, medRxiv), Web of Science and WHO COVID-19 Global literature on COVID-19 to identify completed and ongoing studies up to 1 February 2022. SELECTION CRITERIA: We included RCTs that compared fluvoxamine in addition to standard care (also including no intervention), with standard care (alone or with placebo), or any other active pharmacological comparator with proven efficacy in clinical trials for the treatment of people with confirmed COVID-19, irrespective of disease severity, in both inpatients and outpatients. Co-interventions needed to be the same in both study arms. We excluded studies comparing fluvoxamine to other pharmacological interventions with unproven efficacy. DATA COLLECTION AND ANALYSIS: We assessed risk of bias of primary outcomes using the Cochrane Risk of Bias 2 tool for RCTs. We used GRADE to rate the certainty of evidence to treat people with asymptomatic to severe COVID-19 for the primary outcomes including mortality, clinical deterioration, clinical improvement, quality of life, serious adverse events, adverse events of any grade, and suicide or suicide attempt. MAIN RESULTS: We identified two completed studies with a total of 1649 symptomatic participants. One study was conducted in the USA (study with 152 participants, 80 and 72 participants per study arm) and the other study in Brazil (study with 1497 high-risk participants for progression to severe disease, 741 and 756 participants per study arm) among outpatients with mild COVID-19. Both studies were double-blind, placebo-controlled trials in which participants were prescribed 100 mg fluvoxamine two or three times daily for a maximum of 15 days. We identified five ongoing studies and two studies awaiting classification (due to translation issues, and due to missing published data). We found no published studies comparing fluvoxamine to other pharmacological interventions of proven efficacy. We assessed both included studies to have an overall high risk of bias. Fluvoxamine for the treatment of COVID-19 in inpatients We did not identify any completed studies of inpatients. Fluvoxamine for the treatment of COVID-19 in outpatients Fluvoxamine in addition to standard care may slightly reduce all-cause mortality at day 28 (RR 0.69, 95% CI 0.38 to 1.27; risk difference (RD) 9 per 1000; 2 studies, 1649 participants; low-certainty evidence), and may reduce clinical deterioration defined as all-cause hospital admission or death before hospital admission (RR 0.55, 95% CI 0.16 to 1.89; RD 57 per 1000; 2 studies, 1649 participants; low-certainty evidence). We are very uncertain regarding the effect of fluvoxamine on serious adverse events (RR 0.56, 95% CI 0.15 to 2.03; RD 54 per 1000; 2 studies, 1649 participants; very low-certainty evidence) or adverse events of any grade (RR 1.06, 95% CI 0.82 to 1.37; RD 7 per 1000; 2 studies, 1649 participants; very low-certainty evidence). Neither of the studies reported on symptom resolution (clinical improvement), quality of life or suicide/suicide attempt. AUTHORS' CONCLUSIONS: Based on a low-certainty evidence, fluvoxamine may slightly reduce all-cause mortality at day 28, and may reduce the risk of admission to hospital or death in outpatients with mild COVID-19. However, we are very uncertain regarding the effect of fluvoxamine on serious adverse events, or any adverse events. In accordance with the living approach of this review, we will continually update our search and include eligible trials as they arise, to complete any gaps in the evidence.