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Extra virgin olive oil (EVOO) has healthy benefits for noncommunicable diseases (NCDs). However, limited evidence is available about the effects of liver disease and non-alcoholic fatty liver disease (NAFLD). We evaluate whether dose-increased consumption of EVOO is associated with a lower prevalence of NAFLD and if these effects vary based on body weight. The study included 2436 subjects with a 33% NAFLD prevalence. Daily EVOO was categorized into tertiles: low (0–24 g/day), moderate (25–37 g/day), and high consumption (>37 g/day). Subjects were also classified by body mass index (BMI) as normo-weight (18.5–24.9), overweight (25–29.9), and obese (≥30). Logistic regression analysis was applied to calculate odds ratios (ORs) for NAFLD, considering a 20-gram increment in EVOO intake and accounting for EVOO categories combined with BMI classes. The ORs were 0.83 (0.74;0.93) C.I. p = 0.0018 for continuous EVOO, 0.89 (0.69;1.15) C.I. p = 0.37, and 0.73 (0.55;0.97) C.I. p = 0.03 for moderate and high consumption, respectively, when compared to low consumption. Overall, the percent relative risk reductions (RRR) for NAFLD from low to high EVOO consumption were 18% (16.4%;19.2%) C.I. and 26% (25%;27.4%) C.I. in overweight and obese subjects. High EVOO consumption is associated with a reduced risk of NAFLD. This effect is amplified in overweight subjects and even more in obese subjects. [ABSTRACT FROM AUTHOR]

Polypharmacy, the use of multiple and potentially inappropriate medications, is an increasing problem among older adults. The global polypharmacy prevalence is 34.6% in patients with COVID-19, and polypharmacy in COVID-19 increases with age. The present paper proposes that polypharmacy in older adults with COVID-19 and other comorbid conditions is linked to the medication outcome reporting bias of randomized controlled trials. Outcome reporting bias can occur when treatment efficacy is reported as relative risk reductions, which overestimates medication benefits and exaggerates disease/illness risk reductions compared to unreported absolute risk reductions. The comorbidities common in patients with COVID-19 include high blood pressure, cardiovascular disease, dementia or cerebrovascular disease, and diabetes. Accordingly, the present paper reassesses the relative and absolute risk reductions in clinical trials from a small convenience sample of antihypertension, statin, anticoagulant, and antihyperglycemic medications. Examples demonstrate a wide gap between reported relative risk reductions and unreported absolute risk reductions in medication clinical trials. This paper concludes that medication clinical trial outcome reporting bias is an important upstream factor that contributes to biased medication benefits and poor clinical decision making, leading to polypharmacy in older adults with COVID-19 and other comorbid conditions. Public health campaigns are urgently needed to educate the public about the link between polypharmacy and medication outcome reporting bias. [ABSTRACT FROM AUTHOR]

COVID-19 antiviral medications approved or authorized for emergency use by the U.S. Food and Drug Administration are reported to have high efficacy in preventing severe illness, hospitalizations, and deaths. However, reports for some of these antivirals use relative risk reductions from clinical trials without absolute risk reductions. The present paper reappraises recently published clinical trial data for the COVID-19 antivirals paxlovid, remdesivir, and molnupiravir, and reports absolute risk reductions, relative risk reductions, as well as number needed to treat to reduce severe illness, hospitalizations, and deaths. Relative risk reductions are 88.88% for paxlovid (95% CI: 72.13–95.56%), 86.48% for remdesivir (95% CI: 41.41–96.88%), and 30.41% for molnupiravir (95% CI: 0.81–51.18%), while absolute risk reductions are much lower at 5.73% for paxlovid (95% CI: 3.79–7.68%), 4.58% for remdesivir (95% CI: 1.79–7.38%), and 2.96% for molnupiravir (95% CI: 0.09–5.83%). Low absolute risk reductions and the high number of patients needed to treat to reduce severe COVID-19 infections, hospitalizations, and deaths challenge the clinical efficacy of antivirals approved or authorized by the U.S Food and Drug Administration. These findings apply to other populations with similar control event rates. Accurate information should be disseminated to the public when selecting treatments for COVID-19.

Trial sequential analysis is an adaptation of frequentist sequential methods that can be used to improve inferences from meta-analysis. Trial sequential analysis can help preserve type I and type II error rates at desired levels for analyses conducted before the required information size. Through three case studies recently published in the British Journal of Anaesthesia , we show how trial sequential analysis can inform the interpretation of meta-analyses. Limitations of trial sequential analysis, which also include those of the meta-analysis to which it is applied, must be carefully considered alongside its benefits. [ABSTRACT FROM AUTHOR]

COVID-19 antiviral medications approved or authorized for emergency use by the U.S. Food and Drug Administration are reported to have high efficacy in preventing severe illness, hospitalizations, and deaths. However, reports for some of these antivirals use relative risk reductions from clinical trials without absolute risk reductions. The present paper reappraises recently published clinical trial data for the COVID-19 antivirals paxlovid, remdesivir, and molnupiravir, and reports absolute risk reductions, relative risk reductions, as well as number needed to treat to reduce severe illness, hospitalizations, and deaths. Relative risk reductions are 88.88% for paxlovid (95% CI: 72.13–95.56%), 86.48% for remdesivir (95% CI: 41.41–96.88%), and 30.41% for molnupiravir (95% CI: 0.81–51.18%), while absolute risk reductions are much lower at 5.73% for paxlovid (95% CI: 3.79–7.68%), 4.58% for remdesivir (95% CI: 1.79–7.38%), and 2.96% for molnupiravir (95% CI: 0.09–5.83%). Low absolute risk reductions and the high number of patients needed to treat to reduce severe COVID-19 infections, hospitalizations, and deaths challenge the clinical efficacy of antivirals approved or authorized by the U.S Food and Drug Administration. These findings apply to other populations with similar control event rates. Accurate information should be disseminated to the public when selecting treatments for COVID-19. [ABSTRACT FROM AUTHOR]

Abstract Background Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate serological markers of protection against IGbsD could enable use of efficient clinical trial designs for vaccine development and licensure, without needing to undertake efficacy trials in prohibitively large number of mother-infant dyads. The association of maternal naturally acquired serotype-specific anti-capsular antibodies (IgG) against serotype-specific IGbsD in their infants has been studied in case-control studies. The statistical models used so far to estimate IGbsD risk from these case-control studies assumed that the antibody concentrations measured sharing the same disease status are sampled from the same population, not allowing for differences between mothers colonised by GBS and mothers also potentially infected (e.g urinary tract infection or chorioamnionitis) by GBS during pregnancy. This distinction is relevant as infants born from infected mothers with occult medical illness may be exposed to GBS prior to the mother developing antibodies measured in maternal or infant sera. Methods Unsupervised mixture model averaging (MMA) is proposed and applied here to accurately estimate infant IGbsD risk from case-control study data in presence or absence of antibody concentration subgroups potentially associated to maternal GBS carriage or infection. MMA estimators are compared to non-parametric disease risk estimators in simulation studies and by analysis of two published GBS case-control studies. Results MMA provides more accurate relative risk estimates under a broad range of data simulation scenarios and more accurate absolute disease risk estimates when the proportion of IGbsD cases with high antibody levels is not ignorable. MMA estimates of the relative and absolute disease risk curves are more amenable to clinical interpretation compared to non-parametric estimates with no detectable overfitting of the data. Antibody concentration thresholds predictive of protection from infant IGbsD estimated by MMA from maternal and infant sera are consistent with non-parametric estimates. Conclusions MMA is a flexible and robust method for design, accurate analysis and clinical interpretation of case-control studies estimating relative and absolute IGbsD risk from antibody concentrations measured at or after birth.

Treatment and vaccine efficacy in clinical trials are often reported in the media and medical journals as the relative risk reduction. The present article explains why the relative risk reduction is a misinformative measure that promotes disinformation when reporting efficacy in clinical research studies such as randomized controlled trials for COVID-19 vaccines. The relative risk reduction is based on the relative risk, a proportional measure or ratio used in epidemiologic studies to estimate the probability of a disease associated with an exposure. The present article demonstrates how the relative risk reduction and relative risk obscure the magnitude of disease risk reduction in clinical research. The absolute risk reduction is shown to be a more precise and reliable measure of treatment and vaccine efficacy in clinical research studies. The absolute risk reduction reciprocal also measures the number needed to treat or vaccinate, and is a more accurate measure than the relative risk reduction for comparing risk reductions of clinical studies. Additionally, the present article reviews consequences of COVID-19 vaccine efficacy misinformation disseminated through media reports. The article concludes that relative risk reduction should not be used to measure treatment and vaccine efficacy in clinical trials. What is new?: • Unreliability of relative measures in clinical trials is graphically illustrated, demonstrating constant relative measures as absolute measures change. • Misuse of relative measures in clinical research is historically linked to misinterpretation of Jerome Cornfield’s advice on measuring causative and associative effects. • Consequences of disinformation and misinformation related to COVID-19 vaccine efficacy and modern clinical medicine are described. • The proper use of absolute measures in meta-analyses is explained.

Background: Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate serological markers of protection against IGbsD could enable use of efficient clinical trial designs for vaccine development and licensure, without needing to undertake efficacy trials in prohibitively large number of mother-infant dyads. The association of maternal naturally acquired serotype-specific anti-capsular antibodies (IgG) against serotype-specific IGbsD in their infants has been studied in case-control studies. The statistical models used so far to estimate IGbsD risk from these case-control studies assumed that the antibody concentrations measured sharing the same disease status are sampled from the same population, not allowing for differences between mothers colonised by GBS and mothers also potentially infected (e.g urinary tract infection or chorioamnionitis) by GBS during pregnancy. This distinction is relevant as infants born from infected mothers with occult medical illness may be exposed to GBS prior to the mother developing antibodies measured in maternal or infant sera.Methods: Unsupervised mixture model averaging (MMA) is proposed and applied here to accurately estimate infant IGbsD risk from case-control study data in presence or absence of antibody concentration subgroups potentially associated to maternal GBS carriage or infection. MMA estimators are compared to non-parametric disease risk estimators in simulation studies and by analysis of two published GBS case-control studies.Results: MMA provides more accurate relative risk estimates under a broad range of data simulation scenarios and more accurate absolute disease risk estimates when the proportion of IGbsD cases with high antibody levels is not ignorable. MMA estimates of the relative and absolute disease risk curves are more amenable to clinical interpretation compared to non-parametric estimates with no detectable overfitting of the data. Antibody concentration thresholds predictive of protection from infant IGbsD estimated by MMA from maternal and infant sera are consistent with non-parametric estimates.Conclusions: MMA is a flexible and robust method for design, accurate analysis and clinical interpretation of case-control studies estimating relative and absolute IGbsD risk from antibody concentrations measured at or after birth. [ABSTRACT FROM AUTHOR]

ObjectivesHeterogeneity of effect measures in intervention studies undermines the use of evidence to inform policy. Our objective was to develop a comprehensive algorithm to convert all types of effect measures to one standard metric, relative risk reduction (RRR).Study design and settingThis work was conducted to facilitate synthesis of published intervention effects for our epidemic modeling of the health impact of human immunodeficiency virus [HIV testing and counseling (HTC)]. We designed and implemented an algorithm to transform varied effect measures to RRR, representing the proportionate reduction in undesirable outcomes.ResultsOur extraction of 55 HTC studies identified 473 effect measures representing unique combinations of intervention-outcome-population characteristics, using five outcome metrics: pre-post proportion (70.6%), odds ratio (14.0%), mean difference (10.2%), risk ratio (4.4%), and RRR (0.9%). Outcomes were expressed as both desirable (29.5%, eg, consistent condom use) and undesirable (70.5%, eg, inconsistent condom use). Using four examples, we demonstrate our algorithm for converting varied effect measures to RRR and provide the conceptual basis for advantages of RRR over other metrics.ConclusionOur review of the literature suggests that RRR, an easily understood and useful metric to convey risk reduction associated with an intervention, is underused by original and review studies.

“Necessity is the mother of invention:” An adage was brought to life with the emergence of the mRNA vaccine against the backdrop of the foreboding and mercurial COVID-19 pandemic. Considering a negligible adverse-effect profile and a break-neck manufacturing speed, it shone bright as the ideal vaccine candidate. However, “all that glitters is not gold,” as was evidenced by the significant reactogenicity, a host of multi-systemic side-effects, that are being reported by the vaccine recipients; which is palpably resulting in a shift of emotions for the vaccine, accounting for vaccine hesitancy. Anaphylaxis, antibody-dependent enhancements, and deaths, comprise the most serious side-effects, albeit occurring in sparing numbers. Storage and transportation require fastidious temperatures, rendering it substantially inaccessible to a country like India. The biggest jolt, however, was the unfolding of the biases in reporting vaccine efficacy, as only the attractively high numbers of the relatively equivocal relative risk reduction were reported while keeping at bay the meager numbers of the more forthright absolute risk reduction. Notwithstanding the fallacies, the mRNA vaccine still promises hope; and with the right precautions and finesse, can be potentiated, as “a watched pot never boils.”

The unusual conditions under which COVID-19 vaccines have been tested (in particular social distancing, that cannot be maintained for a long time), possibly makes ARR an unreliable "effectiveness predictor" of vaccination at population level. Keywords: Absolute risk reduction; relative risk reduction; COVID-19; vaccine hesitancy EN Absolute risk reduction relative risk reduction COVID-19 vaccine hesitancy 873 875 3 07/19/22 20220701 NES 220701 Over the last few months, there has been a passionate debate within the scientific community on the risks and the benefits of the vaccines against COVID-19 disease. In this regard, we have to take into account that the effect of COVID-19 vaccines has been evaluated over a background of strict preventive measures at population level (social distancing, hand hygiene, mask wearing) that reduced significantly the background incidence rate and, hence, a steep increase in NNV. Nevertheless, what we primarily need to know during this unprecedented pandemic is that vaccines are safe and that mass vaccination will curb viral circulation in the population or, as recently seen after diffusion of Delta and Omicron variants, will reduce severe COVID-19 cases, preventing health services overload. [Extracted from the article]

In observational studies, as well as in interventional ones, it is frequently necessary to estimate risk that is the association between an observed outcome or event and exposure to one or more factors that may be contributing to the event. Understanding incidence and prevalence are the starting point in any discussion of risk assessment. Incidence rate uses person-time as the denominator rather than a simple count. Ideally, rates and ratios estimated from samples should be presented with their corresponding 95% confidence intervals (CIs). To assess the importance of an individual risk factor, it is necessary to compare the risk of the outcome in the exposed group with that in the nonexposed group. A comparison between risks in different groups can be made by examining either their ratio or the difference between them. The 2 × 2 contingency table comes in handy in the calculation of ratios. Odds ratio (OR) is the ratio of the odds of an event in the exposed group, to the odds of the same event in the nonexposed group. It can range from zero to infinity. When the odds of an outcome in the two groups are identical, then the OR equals one. OR >1 indicates exposure increases risk while OR

After decades of improvement in the outlook for cardiovascular disease (CVD), we are now seeing a plateau. Statins, once believed to be the most important advance in the fight against heart disease, have not mitigated the incidence or prevalence of CVD. Aim: New research into lipid-lowering drugs is not only questioning their usefulness in primary care, but identifying them as harmful, resulting in the development of other diseases. When the original research is critically analyzed, the data do not reveal drugs that significantly reduce the incidence or prevalence for primary prevention of CVD in the United States. Methods: The current article sheds light on our current beliefs into lipid-lowering to treat potential CVD. Through a discussion of the difference between relative risk reduction and absolute risk reduction, the author suggests lifestyle modifications have been and always will be the best way to fight against this deadly chronic disease. Results: There is over 60 years-worth of scientific research that has been desperately trying to identify sugar as the culprit and driver of CVD disease; however, the medical system continues to fight against fat and cholesterol. This article makes the reader question what the US government, in association with the Medical Establishment (American Heart Association, American Diabetes Association and the American College of Cardiology) have been eschewing for the last 60-70 years as it has NOT been working. Conclusion: The time for a culture-wide paradigm change has come. The author suggests this will only happen if Big Pharma and Big Food industries will change their marketing habits from 'purely taste' to 'best for your health'. [ABSTRACT FROM AUTHOR]

To increase power or reduce the number of patients needed in trials studying treatments for psychiatric or mental disorders with a high placebo response rate, we may consider use of the sequential parallel comparison design proposed elsewhere. Because statistical significance does not necessarily imply that the difference between treatment and placebo is of clinical importance, it is always of importance to quantify the treatment effect in clinical trials. When the patient responses are dichotomous, the treatment and other covariates effects are not likely additive. Thus, using a weighted average of the risk differences over two phases may not be a meaningful summary index to measure the treatment effect. To alleviate this concern, we consider use of the relative difference or relative risk reduction to measure the treatment effect. We derive both point and interval estimators for the relative difference by use of the weighted-least-squares estimator and Mantel-Haenszel type estimator. We employ Monte Carlo simulation to evaluate the finite-sample performance of these estimators in a variety of situations. We also include a procedure for testing the homogeneity of the relative difference between phases under the sequential parallel comparison design. We use the placebo-controlled study to assess the efficacy of a low dose of aripiprazole adjunctive to antidepressant therapy in the treatment of patients with major depressive disorder to illustrate the use of estimators developed here. [ABSTRACT FROM AUTHOR]

Relative risk reduction and absolute risk reduction measures in the evaluation of clinical trial data are poorly understood by health professionals and the public. The absence of reported absolute risk reduction in COVID-19 vaccine clinical trials can lead to outcome reporting bias that affects the interpretation of vaccine efficacy. The present article uses clinical epidemiologic tools to critically appraise reports of efficacy in Pfzier/BioNTech and Moderna COVID-19 mRNA vaccine clinical trials. Based on data reported by the manufacturer for Pfzier/BioNTech vaccine BNT162b2, this critical appraisal shows: relative risk reduction, 95.1%; 95% CI, 90.0% to 97.6%; p = 0.016; absolute risk reduction, 0.7%; 95% CI, 0.59% to 0.83%; p < 0.000. For the Moderna vaccine mRNA-1273, the appraisal shows: relative risk reduction, 94.1%; 95% CI, 89.1% to 96.8%; p = 0.004; absolute risk reduction, 1.1%; 95% CI, 0.97% to 1.32%; p < 0.000. Unreported absolute risk reduction measures of 0.7% and 1.1% for the Pfzier/BioNTech and Moderna vaccines, respectively, are very much lower than the reported relative risk reduction measures. Reporting absolute risk reduction measures is essential to prevent outcome reporting bias in evaluation of COVID-19 vaccine efficacy.

"Necessity is the mother of invention:" An adage was brought to life with the emergence of the mRNA vaccine against the backdrop of the foreboding and mercurial COVID-19 pandemic. Considering a negligible adverse-effect profile and a break-neck manufacturing speed, it shone bright as the ideal vaccine candidate. However, "all that glitters is not gold," as was evidenced by the significant reactogenicity, a host of multi-systemic side-effects, that are being reported by the vaccine recipients; which is palpably resulting in a shift of emotions for the vaccine, accounting for vaccine hesitancy. Anaphylaxis, antibody-dependent enhancements, and deaths, comprise the most serious side-effects, albeit occurring in sparing numbers. Storage and transportation require fastidious temperatures, rendering it substantially inaccessible to a country like India. The biggest jolt, however, was the unfolding of the biases in reporting vaccine efficacy, as only the attractively high numbers of the relatively equivocal relative risk reduction were reported while keeping at bay the meager numbers of the more forthright absolute risk reduction. Notwithstanding the fallacies, the mRNA vaccine still promises hope; and with the right precautions and finesse, can be potentiated, as "a watched pot never boils." [ABSTRACT FROM AUTHOR]

The Number Needed To Treat (NNT) is a measure used in epidemiology to convey the effectiveness of an intervention. It is the average number of patients who need to be treated to prevent one bad outcome. It is the reverse of the Absolute Risk Reduction. The lower the NNT, the more effective the intervention. In this article we discuss the concept and limitations of this measure.

Aims: Renal denervation has been shown to lower blood pressure in sham-controlled trials and represents a device-based treatment option for hypertension. We sought to project clinical event reductions after radiofrequency renal denervation using a novel modelling approach., Methods and Results: The Global SYMPLICITY Registry is a global, prospective all-comer registry to evaluate safety and efficacy after renal denervation. For this analysis, change in office systolic blood pressure from baseline was calculated from reported follow-up in the Global SYMPLICITY Registry. Relative risks for death and other cardiovascular events as well as numbers needed to treat for event avoidance were obtained for the respective blood pressure reductions based on previously reported meta-regression analyses for the full cohort and high-risk subgroups including type 2 diabetes, chronic kidney disease, resistant hypertension, and high basal cardiovascular risk. Average baseline office systolic blood pressure and reduction estimates for the full cohort (N = 2651) were 166±25 and -14.8 ± 0.4 mmHg, respectively. Mean reductions in blood pressure ranged from -11.0--21.8 mmHg for the studied high-risk subgroups. Projected relative risks ranged from 0.57 for stroke in the resistant hypertension cohort to 0.92 for death in the diabetes cohort. Significant absolute reductions in major adverse cardiovascular events over 3 years compared with the projected control (8.6 ± 0.7% observed vs. 11.7 ± 0.9% for projected control; P < 0.01) were primarily due to reduced stroke incidence. The robustness of findings was confirmed in sensitivity and scenario analyses., Conclusion: Model-based projections suggest radiofrequency renal denervation for patients with uncontrolled hypertension adds considerable clinical benefit across a spectrum of different cohort characteristics., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)