Purpose: To address the under-reporting of research results, with emphasis on the underreporting/distorted reporting of adverse events in the biomedical research literature.Design/methodology/approach: A four-step app...Purpose: To address the under-reporting of research results, with emphasis on the underreporting/distorted reporting of adverse events in the biomedical research literature.Design/methodology/approach: A four-step approach is used:(1) To identify the characteristics of literature that make it adequate to support policy;(2) to show how each of these characteristics becomes degraded to make inadequate literature;(3) to identify incentives to prevent inadequate literature; and(4) to show policy implications of inadequate literature.Findings: This review has provided reasons for, and examples of, adverse health effects of myriad substances(1) being under-reported in the premiere biomedical literature, or(2) entering this literature in distorted form. Since there is no way to gauge the extent of this under/distorted-reporting, the quality and credibility of the ‘premiere’ biomedical literature is unknown. Therefore, any types of meta-analyses or scientometric analyses of this literaturewill have unknown quality and credibility. The most sophisticated scientometric analysis cannot compensate for a highly flawed database.Research limitations: The main limitation is in identifying examples of under-reporting. There are many incentives for under-reporting and few dis-incentives.Practical implications: Almost all research publications, addressing causes of disease, treatments for disease, diagnoses for disease, scientometrics of disease and health issues, and other aspects of healthcare, build upon previous healthcare-related research published. Many researchers will not have laboratories or other capabilities to replicate or validate the published research, and depend almost completely on the integrity of this literature. If the literature is distorted, then future research can be misguided, and health policy recommendations can be ineffective or worse.Originality/value: This review has examined a much wider range of technical and nontechnical causes for under-reporting of adverse events in the biomedical literature than previous studies.展开更多
The paper looks at the reason for the low reportage of retained abdominal packs following an abdominal operation in a third world country like Nigeria. It is generally agreed that this unfortunate situation is under-r...The paper looks at the reason for the low reportage of retained abdominal packs following an abdominal operation in a third world country like Nigeria. It is generally agreed that this unfortunate situation is under-reported. The reason for under-reporting is now given a socio-cultural perspective. Fear of litigation does not seem to be paramount here like it would be in the western (developed) world. Other explanations like fear of being made a scapegoat for something which may be due to spiritual attacks may be more important. The paper concludes by recommending that the removal of impediments to disclosure of this adverse surgical event will lie in education, discouragement of scapegoatism and improvement in hospital services in the third world.展开更多
Influenza remains a global challenge,imposing a significant burden on society and the economy.Many influenza cases are asymptomatic,leading to greater uncertainty and the under-reporting of cases in influenza transmis...Influenza remains a global challenge,imposing a significant burden on society and the economy.Many influenza cases are asymptomatic,leading to greater uncertainty and the under-reporting of cases in influenza transmission and preventing authorities from taking effective control measures.In this study,we propose a Bayesian hierarchical approach to model and correct under-reporting of influenza cases in Hong Kong,incorporating a discrete-time stochastic,Susceptible-Infected-Recovered-Susceptible(DT-SIRS)model that allows transmission rate to vary over time.The incidence of influenza exhibits seasonality.To examine the relationship between meteorological factors and seasonal influenza activity in subtropical areas,five meteorological factors are included in the model.The proposed model explores the effects of meteorological factors on transmission rates and disease detection covariates on under-reporting,and the inclusion of the DT-SIRS model enables more accurate inference regarding true disease counts.The results demonstrate that under-reporting rates of influenza cases vary significantly in different years and epidemic seasons.In conclusion,our method effectively captures the dynamic behavior of the disease,and we can accurately estimate under-reporting and provide new possibilities for early warning of influenza based on meteorological data and routine surveillance data.展开更多
Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaini...Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes;insurers seeking facts about traffic crash claims;road safety researchers to access traffic crash reliable database;decision makers to develop long-term, statewide strategic plans for traffic and highway safety;and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.展开更多
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 underreporting is acknowledged in epidemiology,the guida...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 underreporting 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 e Infected e 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 2016e2017 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 underreporting 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.展开更多
The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases,beyond constant fear of the collapse in their health systems.Since the beginnin...The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases,beyond constant fear of the collapse in their health systems.Since the beginning of the pandemic,researchers and authorities are mainly concerned with carrying out quantitative studies(modeling and predictions)overcoming the scarcity of tests that lead us to under-reporting cases.To address these issues,we introduce a Bayesian approach to the SIR model with correction for underreporting in the analysis of COVID-19 cases in Brazil.The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period,along with the more likely date when the pandemic peak may occur.Several under-reporting scenarios were considered in the simulation study,showing how impacting is the lack of information in the modeling.展开更多
文摘Purpose: To address the under-reporting of research results, with emphasis on the underreporting/distorted reporting of adverse events in the biomedical research literature.Design/methodology/approach: A four-step approach is used:(1) To identify the characteristics of literature that make it adequate to support policy;(2) to show how each of these characteristics becomes degraded to make inadequate literature;(3) to identify incentives to prevent inadequate literature; and(4) to show policy implications of inadequate literature.Findings: This review has provided reasons for, and examples of, adverse health effects of myriad substances(1) being under-reported in the premiere biomedical literature, or(2) entering this literature in distorted form. Since there is no way to gauge the extent of this under/distorted-reporting, the quality and credibility of the ‘premiere’ biomedical literature is unknown. Therefore, any types of meta-analyses or scientometric analyses of this literaturewill have unknown quality and credibility. The most sophisticated scientometric analysis cannot compensate for a highly flawed database.Research limitations: The main limitation is in identifying examples of under-reporting. There are many incentives for under-reporting and few dis-incentives.Practical implications: Almost all research publications, addressing causes of disease, treatments for disease, diagnoses for disease, scientometrics of disease and health issues, and other aspects of healthcare, build upon previous healthcare-related research published. Many researchers will not have laboratories or other capabilities to replicate or validate the published research, and depend almost completely on the integrity of this literature. If the literature is distorted, then future research can be misguided, and health policy recommendations can be ineffective or worse.Originality/value: This review has examined a much wider range of technical and nontechnical causes for under-reporting of adverse events in the biomedical literature than previous studies.
文摘The paper looks at the reason for the low reportage of retained abdominal packs following an abdominal operation in a third world country like Nigeria. It is generally agreed that this unfortunate situation is under-reported. The reason for under-reporting is now given a socio-cultural perspective. Fear of litigation does not seem to be paramount here like it would be in the western (developed) world. Other explanations like fear of being made a scapegoat for something which may be due to spiritual attacks may be more important. The paper concludes by recommending that the removal of impediments to disclosure of this adverse surgical event will lie in education, discouragement of scapegoatism and improvement in hospital services in the third world.
文摘Influenza remains a global challenge,imposing a significant burden on society and the economy.Many influenza cases are asymptomatic,leading to greater uncertainty and the under-reporting of cases in influenza transmission and preventing authorities from taking effective control measures.In this study,we propose a Bayesian hierarchical approach to model and correct under-reporting of influenza cases in Hong Kong,incorporating a discrete-time stochastic,Susceptible-Infected-Recovered-Susceptible(DT-SIRS)model that allows transmission rate to vary over time.The incidence of influenza exhibits seasonality.To examine the relationship between meteorological factors and seasonal influenza activity in subtropical areas,five meteorological factors are included in the model.The proposed model explores the effects of meteorological factors on transmission rates and disease detection covariates on under-reporting,and the inclusion of the DT-SIRS model enables more accurate inference regarding true disease counts.The results demonstrate that under-reporting rates of influenza cases vary significantly in different years and epidemic seasons.In conclusion,our method effectively captures the dynamic behavior of the disease,and we can accurately estimate under-reporting and provide new possibilities for early warning of influenza based on meteorological data and routine surveillance data.
文摘Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes;insurers seeking facts about traffic crash claims;road safety researchers to access traffic crash reliable database;decision makers to develop long-term, statewide strategic plans for traffic and highway safety;and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.
基金supported by National Institute of General Medical Sciences of the National Institutes of Health,United States(Award numbers P20GM130418 and U54GM104944).
文摘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 underreporting 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 e Infected e 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 2016e2017 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 underreporting 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.
文摘The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases,beyond constant fear of the collapse in their health systems.Since the beginning of the pandemic,researchers and authorities are mainly concerned with carrying out quantitative studies(modeling and predictions)overcoming the scarcity of tests that lead us to under-reporting cases.To address these issues,we introduce a Bayesian approach to the SIR model with correction for underreporting in the analysis of COVID-19 cases in Brazil.The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period,along with the more likely date when the pandemic peak may occur.Several under-reporting scenarios were considered in the simulation study,showing how impacting is the lack of information in the modeling.
