User-Centered Design for Designing and Evaluating a Prototype of a Data Collection Tool to Submit Information About Incidents of Violence Against Sex Workers: Multiple Methods Approach.

Background: Sex workers face an epidemic of violence in the United States. However, violence against sex workers in the United States is underreported. Sex workers hesitate to report it to the police because they are frequently punished themselves; therefore, an alternative for reporting is needed. Objective: We aim to apply human-centered design methods to create and evaluate the usability of the prototype interface for ReportVASW (violence against sex worker, VASW) and identify opportunities for improvement. Methods: This study explores ways to improve the prototype of ReportVASW, with particular attention to ways to improve the data collection tool. Evaluation methods included cognitive walkthrough, system usability scale, and heuristic evaluation. Results: End users were enthusiastic about the idea of a website to document violence against sex workers. ReportVASW scored 90 on the system usability scale. The tool scored neutral on consistency, and all other responses were positive toward the app, with most being strong. Conclusions: Many opportunities to improve the interface were identified. Multiple methods identified multiple issues to address. Most changes are not overly complex, and the majority were aesthetic or minor. Further development of the ReportVASW data collection tool is worth pursuing.

Estimating the Real Impact of the COVID-19 Pandemic in Kazakhstan: Factors Associated with Detection of the "True Infections".

The COVID-19 pandemic is ongoing worldwide, and various case and death numbers are being reported to track its spread. However, the number of actual cases is uncertain due to under-reporting. Using mortality data as a more reliable indicator, this study in Kazakhstan evaluated the extent of under-reporting and under-detection of COVID-19 cases from March 2020 to September 2022 using back-casting and capture-recapture methods. The results indicate that official case reporting in Kazakhstan significantly underestimates the number of infections by at least 50%. The study also suggests that improved testing capabilities may have led to a decrease in the percentage of unreported cases, however, early in the pandemic, Kazakhstan faced significant testing shortages. The study presents a mathematical model based on mortality data that highlights the severe under-reporting of COVID-19 cases in Kazakhstan and argues that understanding the true estimate of actual cases could aid in making informed decisions to end the pandemic.

Excess death estimates compared with state-reported and observed COVID-19 deaths, New Jersey and the United States, 2020-2022.

Deaths associated with COVID-19 in the United States are currently estimated to be over 1.2 million, but the true burden of mortality due to the SARS-CoV-2 virus is unknown. Methods for identifying and reporting deaths related to COVID-19 differ between jurisdictions, and concerns about overreporting and underreporting exist. Excess death estimates for the pandemic period, based on data from the National Center for Health Statistics, may be used to approximate the number of COVID-19-associated deaths. In this analysis, we first describe the process by which the New Jersey Department of Health identified, classified, and reported COVID-19-associated deaths from January 2020 through December 2022. The National Center for Health Statistics' excess deaths estimates are first compared with New Jersey's reported COVID-19-associated deaths, and then with the observed COVID-19-associated deaths in the entire United States, by month, from January 2020 through December 2022. New Jersey's reported COVID-19-associated deaths (n = 35,555) accounted for (and slightly exceeded) the state's excess deaths estimated by the National Center for Health Statistics for 2020-2022 (n = 30,365). However, the overall number of United States observed COVID-19 deaths for 2020-2022 (n = 1,094,230) for the study period did not account for all estimated excess deaths in the nation for the same period (n = 1,233,366). The general congruence of New Jersey's reported COVID-19 deaths and the National Center for Health Statistics' excess death estimates may be due in part to New Jersey's early detailed classification system for identifying and reporting deaths associated with COVID-19, leading to more accurate COVID-19 death reporting by the state.

Application of compound poisson model to estimate underreported risk of non-communicable diseases in underdeveloped areas.

Background: Hypertension and diabetes are major components of non-communicable diseases (NCDs), with a substantial number of patients residing in underdeveloped areas. Limited medical resources in these areas often results in underreporting of disease prevalence, masking the true extent of diseases. Taking the underdeveloped Liangshan Yi Autonomous Prefecture in China as an example, this study aimed to correct the underreported prevalence of hypertension and type 2 diabetes so as to provide inspiration for the allocation of medical resources in such areas. Methods: Assuming the true number of patients in each area follows a Poisson distribution, we applied a Compound Poisson Model based on Clustering of Data Quality (CPM-CDQ) to estimate the potential true prevalence of hypertension and diabetes, as well as the registration rate of existing patients. Specifically, a hierarchical clustering approach was utilized to group the counties based on the data quality, and then the registration rate of the cluster with the best data quality was used as a priori information for the model. The model parameters were estimated by the maximum likelihood method. Sensitivity analyses were performed to test the robustness of the model. Results: The estimated prevalence of hypertension in the entire Liangshan Prefecture from 2018 to 2020 ranged from 24.59 % to 25.28 %, and for diabetes, it ranged from 4.95 % to 8.42 %. The registration rates for hypertension and diabetes were 14.10 % to 24.59 % and 15.98 % to 29.12 %, respectively. Additionally, the accuracy of clustering the counties with the best data quality had a significant impact on the performance of the model. Conclusion: Liangshan Prefecture is experiencing a significant high prevalence of hypertension and diabetes, accompanied by a concerningly low registration rate. The CPM-CDQ proved useful for assessing underreporting risks and facilitating targeted interventions for NCDs control and prevention, particularly in underdeveloped areas.

A Quality Improvement Project to Enhance Emergency Nurse Workplace Violence Reporting.

INTRODUCTION: Workplace violence is a pervasive, perpetual, and underreported problem in the emergency department. Nurses experience devastating physical and psychological consequences owing to workplace violence exposure. Understanding workplace violence is challenging due to nurse underreporting. Improvement in capturing workplace violence cases can help drive data-informed, sustainable solutions to workplace violence prevention. The purpose of this project was to better capture the full extent of workplace violence in the emergency department through enhanced nurse workplace violence reporting. METHODS: In collaboration with emergency nurses and management, the project team used a multi-interventional approach and designed a feasible electronic reporting instrument to capture workplace violence events in real time by reducing nurse reporting barriers. Participating nurses accessed the instrument by scanning a quick-response code with their mobile phones. This code was strategically located on wall flyers and individual name badge stickers. Data were collected using Qualtrics software and analyzed using SPSS Statistics, which summarized the descriptive statistics. RESULTS: Two months after implementation, the electronic reporting instrument recorded 94 quick response code scans and 59 workplace violence reports, resulting in a 1080% increase in ED workplace violence reporting compared with the 2 months prior to implementation. The 59 workplace violence reports comprised 78% registered nurses, 19% emergency medical technicians, and 3% other health care workers. Workplace violence events were most frequent from 12:01 pm to 6:00 pm in treatment rooms (37%), triage (29%), waiting room (19%), behavioral health section (15%), and hallway (12%). DISCUSSION: A multi-interventional approach, coupled with a feasible, readily available electronic reporting instrument, enhanced ED workplace violence reporting and facilitated a better capture of ED workplace violence. Although nurse underreporting is still likely occurring, an emergence of workplace violence data trends can provide data-driven solutions in workplace violence prevention. Continued efforts to reduce the barriers that inhibit reporting are needed to foster a safe and supportive culture surrounding workplace violence reporting.

Impact of cross-reactivity and herd immunity on SARS-CoV-2 pandemic severity.

Public health systems reported low mortality from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in East Asia, in low-income countries, and for children during the first year of the SARS-CoV-2 pandemic. These reports led commentators to suggest that cross-reactive immunity from prior exposure to other pathogens reduced fatality risk. Resolution of initial infection waves also contributed to speculation that herd immunity prevented further waves prior to vaccination. Serology instead implied that immunity was too limited to achieve herd immunity and that there was little impact from cross-reactive protection. Paediatric deaths exceeded those from influenza, with higher age-specific fatality risk in lower-income nations and similar fatality risk in East Asia compared with demographically similar regions. Neither pre-outbreak exposure to related pathogens nor immunity induced by initial infection waves are necessarily a reliable response to future pathogen outbreaks. Preparedness for future pathogen outbreaks should instead focus on strategies such as voluntary behavioural changes, nonpharmaceutical interventions, and vaccination.

Underreporting of Cases in the COVID-19 Outbreak of Borriana (Spain) during Mass Gathering Events in March 2020: A Cross-Sectional Study.

Determining the number of cases of an epidemic is the first function of epidemiological surveillance. An important underreporting of cases was observed in many locations during the first wave of the COVID-19 pandemic. To estimate this underreporting in the COVID-19 outbreak of Borriana (Valencia Community, Spain) in March 2020, a cross-sectional study was performed in June 2020 querying the public health register. Logistic regression models were used. Of a total of 468 symptomatic COVID-19 cases diagnosed in the outbreak through anti-SARS-CoV-2 serology, 36 cases were reported (7.7%), resulting in an underreporting proportion of 92.3% (95% confidence interval [CI], 89.5-94.6%), with 13 unreported cases for every reported case. Only positive SARS-CoV-2 polymerase chain reaction cases were predominantly reported due to a limited testing capacity and following a national protocol. Significant factors associated with underreporting included no medical assistance for COVID-19 disease, with an adjusted odds ratio [aOR] of 10.83 (95% CI 2.49-47.11); no chronic illness, aOR = 2.81 (95% CI 1.28-6.17); middle and lower social classes, aOR = 3.12 (95% CI 1.42-6.85); younger age, aOR = 0.97 (95% CI 0.94-0.99); and a shorter duration of illness, aOR = 0.98 (95% CI 0.97-0.99). To improve the surveillance of future epidemics, new approaches are recommended.

Estimating the human bottleneck for contact tracing.

The SARS-CoV-2 pandemic has highlighted the importance of contact tracing for epidemiological mitigation. Contact tracing interviews (CTIs) typically rely on episodic memory, which is prone to decline over time. Here, we provide a quantitative estimate of reporting decline for age- and gender-representative samples from the United Kingdom and Germany, emulating >15,000 CTIs. We find that the number of reported contacts declines as a power function of recall delay and is significantly higher for younger subjects and for those who used memory aids, such as a scheduler. We further find that these factors interact with delay: Older subjects and those who made no use of memory aids have steeper decline functions. These findings can inform epidemiological modeling and policies in the context of infectious diseases.

A simple modification to the classical SIR model to estimate the proportion of under-reported infections using case studies in flu and COVID-19.

Background: Under-reporting and, thus, uncertainty around the true incidence of health events is common in all public health reporting systems. While the problem of under-reporting is acknowledged in epidemiology, the guidance and methods available for assessing and correcting the resulting bias are obscure. Objective: We aim to design a simple modification to the Susceptible - Infected - Removed (SIR) model for estimating the fraction or proportion of reported infection cases. Methods: The suggested modification involves rescaling of the classical SIR model producing its mathematically equivalent version with explicit dependence on the reporting parameter (true proportion of cases reported). We justify the rescaling using the phase plane analysis of the SIR model system and show how this rescaling parameter can be estimated from the data along with the other model parameters. Results: We demonstrate how the proposed method is cross-validated using simulated data with known disease cases and then apply it to two empirical reported data sets to estimate the fraction of reported cases in Missoula County, Montana, USA, using: (1) flu data for 2016-2017 and (2) COVID-19 data for fall of 2020. Conclusions: We establish with the simulated and COVID-19 data that when most of the disease cases are presumed reported, the value of the additional reporting parameter in the modified SIR model is close or equal to one, so that the original SIR model is appropriate for data analysis. Conversely, the flu example shows that when the reporting parameter is close to zero, the original SIR model is not accurately estimating the usual rate parameters, and the re-scaled SIR model should be used. This research demonstrates the role of under-reporting of disease data and the importance of accounting for under-reporting when modeling simulated, endemic, and pandemic disease data. Correctly reporting the "true" number of disease cases will have downstream impacts on predictions of disease dynamics. A simple parameter adjustment to the SIR modeling framework can help alleviate bias and uncertainty around crucial epidemiological metrics (e.g.: basic disease reproduction number) and public health decision making.

Public health management of pertussis in adults: Practical challenges and future strategies.

A panel of 24 international experts met in July 2022 to discuss challenges associated with pertussis detection, monitoring, and vaccination in adults; conclusions from this meeting are presented. There has been a shift in the epidemiology of pertussis toward older children and adults. This shift has been attributed to the waning of infection- or vaccine-induced immunity, newer detection techniques causing detection bias, and possibly the replacement of whole-cell pertussis with acellular vaccines in high-income countries, which may lead to immunity waning more quickly. The burden of adult pertussis is still likely under-ascertained due to widespread under-recognition by healthcare professionals (HCPs), under-diagnosis, and under-reporting in this age group. Non-standardized testing guidance and varied case definitions have contributed to under-reporting. Key barriers to HCP engagement with the tetanus, diphtheria, and pertussis (Tdap) vaccine include low awareness, lack of time/funding, and lack of motivation due to low prioritization of Tdap.

An Awareness of Pharmacovigilance Among Healthcare Professionals Due to an Underreporting of Adverse Drug Reactions Issue: A Systematic Review of the Current State, Obstacles, and Strategy.

BACKGROUND: Healthcare professionals play an essential role in reporting adverse drug reactions as part of pharmacovigilance activities. However, adverse drug reactions reported by healthcare professionals remain low. OBJECTIVE: The aim of this systematic review was to investigate healthcare professionals' knowledge, awareness, attitude, and practice on pharmacovigilance and adverse drug reaction reporting, explore the causes of the underreporting issue, and provide improvement strategies. METHODS: This systematic review was conducted using four electronic databases for original papers, including PubMed, Scopus, Google Scholar, and Scholar ID. Recent publications from 1st January 2012 to 31st December 2022 were selected. The following terms were used in the search: "awareness", "knowledge", "adverse drug reaction", "pharmacovigilance", "healthcare professional", and "underreporting factor". Articles were chosen, extracted, and reviewed by the two authors. RESULTS: Twenty-five studies were selected for systematic review. This review found that 24.8%-73.33% of healthcare professionals were unaware of the National Pharmacovigilance Center. Around 20%-95.7% of healthcare professionals have a positive attitude toward pharmacovigilance and adverse drug reaction reporting, while 12%-60.8% of healthcare professionals have experience reporting any adverse drug reaction in their practice. The most frequently highlighted barriers to pharmacovigilance were a lack of awareness and knowledge regarding what, when, and to whom to report. CONCLUSION: Underreporting issues require immediate attention among healthcare professionals due to a lack of awareness and knowledge of pharmacovigilance and adverse drug reaction reporting. Educational and training program interventions have been suggested by most studies to address these issues.

Predictors of Concussion Symptom Reporting Intention among Collegiate Athletes.

CONTEXT: Underreporting of concussion symptoms in college athletics presents a challenge for sports medicine clinicians in evaluating and diagnosing such injuries. Some athletes do not report concussion symptoms because they do not recognize that they have a brain injury, however many athletes intentionally withhold symptoms to avoid removal from sport participation. OBJECTIVE: To examine individual factors that influence college athletes' intentions to report concussion symptoms. DESIGN: Cross-sectional study. SETTING: Collegiate athletics. PARTICIPANTS: 2,649 student-athletes from 23 sports, across 22 colleges/universities. MAIN OUTCOME MEASURES: The primary outcome was intention to report concussion symptoms. Predictor variables included demographics (age, race/ethnicity, sex, sport type, number of years in sport, number of previous concussions, and perceived concussion symptom knowledge), athletic identity, attitudes toward symptom reporting, perceived social pressure (injunctive and descriptive norms), and perceived behavioral control (capacity and autonomy). RESULTS: Hierarchical ordinary least squares regression revealed positive effects of attitude (b = .063; P = .005), descriptive norms (b = .131; P < .001), injunctive norms (b = .107; P < .001), and capacity (b = .196; P < .001) on intention to report symptoms. Athletic identity and participation in collision sports had small negative indirect effects on intention, while perceived concussion knowledge had a small positive indirect effect. The full regression model explained 14.24% of the variance in concussion reporting intention. CONCLUSIONS: These findings may help clinicians develop more focused interventions that address key social and individual determinants of underreporting, including attitude, injunctive and descriptive norms, and capacity to report. Athletic identity, sport type, and perceived understanding of concussion symptoms also influence reporting intention to a lesser extent. Previous research in this area has often failed to address a diverse population of college-age athletes from different sports and NCAA divisions.

The impact of underreported infections on vaccine effectiveness estimates derived from retrospective cohort studies.

BACKGROUND: Surveillance data and vaccination registries are widely used to provide real-time vaccine effectiveness (VE) estimates, which can be biased due to underreported (i.e. under-ascertained and under-notified) infections. Here, we investigate how the magnitude and direction of this source of bias in retrospective cohort studies vary under different circumstances, including different levels of underreporting, heterogeneities in underreporting across vaccinated and unvaccinated, and different levels of pathogen circulation. METHODS: We developed a stochastic individual-based model simulating the transmission dynamics of a respiratory virus and a large-scale vaccination campaign. Considering a baseline scenario with 22.5% yearly attack rate and 30% reporting ratio, we explored fourteen alternative scenarios, each modifying one or more baseline assumptions. Using synthetic individual-level surveillance data and vaccination registries produced by the model, we estimated the VE against documented infection taking as reference either unvaccinated or recently vaccinated individuals (within 14 days post-administration). Bias was quantified by comparing estimates to the known VE assumed in the model. RESULTS: VE estimates were accurate when assuming homogeneous reporting ratios, even at low levels (10%), and moderate attack rates (<50%). A substantial downward bias in the estimation arose with homogeneous reporting and attack rates exceeding 50%. Mild heterogeneities in reporting ratios between vaccinated and unvaccinated strongly biased VE estimates, downward if cases in vaccinated were more likely to be reported and upward otherwise, particularly when taking as reference unvaccinated individuals. CONCLUSIONS: In observational studies, high attack rates or differences in underreporting between vaccinated and unvaccinated may result in biased VE estimates. This study underscores the critical importance of monitoring data quality and understanding biases in observational studies, to more adequately inform public health decisions.

Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020-2022 pandemic.

The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.

Cumulative incidence of cardiac surgery associated with exposure to benfluorex: A retrospective analysis based on compensation claims data.

Data on retrospective compensation claims for injuries caused by pharmaceutical drugs are prone to selection and reporting biases. Nevertheless, this case study of the antidiabetic drug benfluorex shows that such data can be used to estimate the cumulative incidence of drug-related injury, and to provide insights into its epidemiology. To this end, we develop a modelling framework for under-reporting of retrospective claims for compensation arising from drug damage. The model involves a longitudinal component related to attrition of cases over time, and a cross-sectional component related to incomplete reporting. We apply this model to cardiac valve surgery necessitated by exposure to benfluorex. Benfluorex was marketed in France between 1976 and 2009, when it was withdrawn because it caused valvular heart disease. A scandal erupted in 2010 over the scale of the damage caused by the drug. Since then, no further estimates of cumulative incidence have been published, though thousands of claims for compensation have been processed. The analysis combines compensation claims data and sociological survey data on benfluorex users, together with data on benfluorex sales and duration of treatment. We find a threshold of toxicity at about 6 months' exposure, and that at least 1690 individuals (95% CI 1290 to 2320) needed heart surgery to replace or repair valves damaged by exposure to benfluorex in France: a cumulative incidence of 3.68 per 10,000 (95% CI 2.68 to 5.34) benfluorex users or 3.22 per 10,000 (95% CI 2.48 to 4.39) person-years at risk above the exposure threshold. While these findings are tentative, they are consistent with those obtained previously using very different methods.

Assessment of Knowledge, Attitude, and Practice of Pharmacovigilance and Materiovigilance among Oral Health Practitioners in India.

Aim: This electronic survey was carried out to assess the knowledge, attitude, and practices among the oral health practitioners. Methods: A questionnaire survey was conducted among undergraduates, graduated practitioners, and specialist practitioners. A Google Form that included a specified instruction format, informed consent with a clear description of the purpose of the study, and questions in different categories were sent through Whatsapp and email. Descriptive statistics were used to analyze responses. The association of knowledge and attitude with respect to oral health professionals was analyzed with the Chi-square test. Results: The questionnaire was completed by 570 participants. Thirty percent of participants encountered ADR related to the drugs available in the market and 33.2% of participants encountered adverse reaction related to materials used in practice. ADR should be reported as soon as it is detected, according to this study about 12.1% of participants reported the ADRs, and this reporting is primarily for patient safety. Almost 66.8% of participants perceived that dental products can cause ADR owing to a lack of information about ADR and reporting procedures, or due to fear of legal concerns. Only about 9.5% of the participants had previously attended an ADR workshop. Conclusion: Oral health practitioners have a general understanding of ADR; however, there is substantial evidence of underreporting and a lack of reporting system information. Organizing an orientation program and raising awareness about ADR reporting could help improve spontaneous reporting and better patient care.

Understanding the impact of adult pertussis and current approaches to vaccination: A narrative review and expert panel recommendations.

Pertussis has several notable consequences, causing economic burden, increased strain on healthcare facilities, and reductions in quality of life. Recent years have seen a trend toward an increase in pertussis cases affecting older children and adults. To boost immunity, and protect vulnerable populations, an enduring approach to vaccination has been proposed, but gaps remain in the evidence surrounding adult vaccination that are needed to inform such a policy. Gaps include: the true incidence of pertussis and its complications in adults; regional variations in disease recognition and reporting; and incidence of severe disease, hospitalizations, and deaths in older adults. Better data on the efficacy/effectiveness of pertussis vaccination in adults, duration of protection, and factors leading to poor vaccine uptake are needed. Addressing the critical evidence gaps will help highlight important areas of unmet need and justify the importance of adult pertussis vaccination to healthcare professionals, policymakers, and payers.

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE: Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients. METHODS: We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies. RESULTS: A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test). SIGNIFICANCE: Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.

Under-reporting of subjective symptoms and its prognostic value: a pooled analysis of 12 cancer clinical trials.

BACKGROUND: Oncologists tend to under-report subjective symptoms during cancer treatment. This study describes the under-reporting rate of selected symptoms and explores its association with overall survival (OS). A secondary aim is to test the association of patient-reported symptoms with OS. PATIENTS AND METHODS: This is a post hoc analysis on data pooled from 12 randomized trials, promoted by the National Cancer Institute of Naples (Italy), enrolling patients between 2002 and 2019, with published primary analyses. Occurrence and grade of six side-effects (anorexia, nausea, vomiting, constipation, diarrhea and fatigue) reported by physicians were compared with corresponding symptoms reported by patients in quality-of-life (QoL) questionnaires. Under-reporting was defined as the rate of cases reported grade 0 by the physician while grade ≥1 by the patient. Prognostic value was tested in a multivariable model stratified by trial, including age, sex and performance status as confounders. A landmark threshold was defined for OS analyses. RESULTS: 3792 patients with advanced lung, ovarian, pancreatic, breast or colorectal cancer were pooled; 2603 (68.6%) were eligible having at least one toxicity assessment and one QoL questionnaire, before the first planned disease restaging. Concordance between physicians' and patients' reporting was low with Cohen's k coefficients ranging from 0.03 (fatigue) to 0.33 (vomiting). Under-reporting ranged from 52.7% (nausea) to 80.5% (anorexia), and was not associated with OS. Patient-reported anorexia, vomiting and fatigue ('a little' or more) were significantly associated with shorter OS. CONCLUSIONS: Under-reporting of treatment side-effects is frequent, but it does not affect OS. Patients' reported symptoms should be used for prognostic evaluation.