Emily Riseberg, Rachel D Melamed, Katherine A James, Tanya L Alderete, Laura Corlin
Objectives: Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes.
Methods: We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models.
Results: From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose.
Conclusions: We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.
{"title":"Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes.","authors":"Emily Riseberg, Rachel D Melamed, Katherine A James, Tanya L Alderete, Laura Corlin","doi":"10.1515/em-2022-0133","DOIUrl":"https://doi.org/10.1515/em-2022-0133","url":null,"abstract":"<p><strong>Objectives: </strong>Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes.</p><p><strong>Methods: </strong>We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models.</p><p><strong>Results: </strong>From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose.</p><p><strong>Conclusions: </strong>We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"12 1","pages":"20220133"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10292771/pdf/em-12-1-em-2022-0133.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10352001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sachin Kumar, Vijendra Pratap Singh, S. Pal, Priya Jaiswal
Abstract Objective The outbreak of the coronavirus caused major problems in more than 151 countries around the world. An important step in the fight against coronavirus is the search for infected people. The goal of this article is to predict COVID-19 infectious patients. Methods We implemented DenseNet201, available on cloud platform, as a learning network. DenseNet201 is a 201-layer networkthat. is trained on ImageNet. The input size of pre-trained DenseNet201 images is 224 × 224 × 3. Results Implementation of DenseNet201 was effectively performed based on 80 % of the training X-rays and 20 % of the X-rays of the test phases, respectively. DenseNet201 shows a good experimental result with an accuracy of 99.24 % in 7.47 min. To measure the computational efficiency of the proposed model, we collected more than 6,000 noise-free data infected by tuberculosis, COVID-19, and uninfected healthy chests for implementation. Conclusions DenseNet201 available on the cloud platform has been used for the classification of COVID-19-infected patients. The goal of this article is to demonstrate how to achieve faster results.
{"title":"Energy-efficient model “DenseNet201 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients","authors":"Sachin Kumar, Vijendra Pratap Singh, S. Pal, Priya Jaiswal","doi":"10.1515/em-2021-0047","DOIUrl":"https://doi.org/10.1515/em-2021-0047","url":null,"abstract":"Abstract Objective The outbreak of the coronavirus caused major problems in more than 151 countries around the world. An important step in the fight against coronavirus is the search for infected people. The goal of this article is to predict COVID-19 infectious patients. Methods We implemented DenseNet201, available on cloud platform, as a learning network. DenseNet201 is a 201-layer networkthat. is trained on ImageNet. The input size of pre-trained DenseNet201 images is 224 × 224 × 3. Results Implementation of DenseNet201 was effectively performed based on 80 % of the training X-rays and 20 % of the X-rays of the test phases, respectively. DenseNet201 shows a good experimental result with an accuracy of 99.24 % in 7.47 min. To measure the computational efficiency of the proposed model, we collected more than 6,000 noise-free data infected by tuberculosis, COVID-19, and uninfected healthy chests for implementation. Conclusions DenseNet201 available on the cloud platform has been used for the classification of COVID-19-infected patients. The goal of this article is to demonstrate how to achieve faster results.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"752 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76887951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuzi Zhang, Lin Ge, Lance A. Waller, Robert H. Lyles
Abstract In epidemiological studies, the capture-recapture (CRC) method is a powerful tool that can be used to estimate the number of diseased cases or potentially disease prevalence based on data from overlapping surveillance systems. Estimators derived from log-linear models are widely applied by epidemiologists when analyzing CRC data. The popularity of the log-linear model framework is largely associated with its accessibility and the fact that interaction terms can allow for certain types of dependency among data streams. In this work, we shed new light on significant pitfalls associated with the log-linear model framework in the context of CRC using real data examples and simulation studies. First, we demonstrate that the log-linear model paradigm is highly exclusionary. That is, it can exclude, by design, many possible estimates that are potentially consistent with the observed data. Second, we clarify the ways in which regularly used model selection metrics (e.g., information criteria) are fundamentally deceiving in the effort to select a “best” model in this setting. By focusing attention on these important cautionary points and on the fundamental untestable dependency assumption made when fitting a log-linear model to CRC data, we hope to improve the quality of and transparency associated with subsequent surveillance-based CRC estimates of case counts.
{"title":"On some pitfalls of the log-linear modeling framework for capture-recapture studies in disease surveillance","authors":"Yuzi Zhang, Lin Ge, Lance A. Waller, Robert H. Lyles","doi":"10.1515/em-2023-0019","DOIUrl":"https://doi.org/10.1515/em-2023-0019","url":null,"abstract":"Abstract In epidemiological studies, the capture-recapture (CRC) method is a powerful tool that can be used to estimate the number of diseased cases or potentially disease prevalence based on data from overlapping surveillance systems. Estimators derived from log-linear models are widely applied by epidemiologists when analyzing CRC data. The popularity of the log-linear model framework is largely associated with its accessibility and the fact that interaction terms can allow for certain types of dependency among data streams. In this work, we shed new light on significant pitfalls associated with the log-linear model framework in the context of CRC using real data examples and simulation studies. First, we demonstrate that the log-linear model paradigm is highly exclusionary. That is, it can exclude, by design, many possible estimates that are potentially consistent with the observed data. Second, we clarify the ways in which regularly used model selection metrics (e.g., information criteria) are fundamentally deceiving in the effort to select a “best” model in this setting. By focusing attention on these important cautionary points and on the fundamental untestable dependency assumption made when fitting a log-linear model to CRC data, we hope to improve the quality of and transparency associated with subsequent surveillance-based CRC estimates of case counts.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sprockel Diaz Johm Jaime, Hector Fabio Restrepo Guerrero, J. J. Fernández
Abstract Objective To identify prognostic markers by applying machine learning strategies to the feature selection. Methods An observational, retrospective, multi-center study that included hospitalized patients with a confirmed diagnosis of COVID-19 in three hospitals in Colombia. Eight strategies were applied to select prognostic-related characteristics. Eight logistic regression models were built from each set of variables and the predictive ability of the outcome was evaluated. The primary endpoint was transfer to intensive care or in-hospital death. Results The database consisted of 969 patients of which 486 had complete data. The main outcome occurred in 169 cases. The development database included 220 patients, 137 (62.3%) were men with a median age of 58.2, 39 (17.7%) were diabetic, 62 (28.2%) had high blood pressure, and 32 (14.5%) had chronic lung disease. Thirty-three variables were identified as prognostic markers, and those selected most frequently were: LDH, PaO2/FIO2 ratio, CRP, age, neutrophil and lymphocyte counts, respiratory rate, oxygen saturation, ferritin, and HCO3. The eight logistic regressions developed were validated on 266 patients in whom similar results were reached (accuracy: 65.8–72.9%). Conclusions The combined use of strategies for selecting characteristics through machine learning techniques makes it possible to identify a broad set of prognostic markers in patients hospitalized for COVID-19 for death or hospitalization in intensive care.
{"title":"Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19","authors":"Sprockel Diaz Johm Jaime, Hector Fabio Restrepo Guerrero, J. J. Fernández","doi":"10.1515/em-2022-0132","DOIUrl":"https://doi.org/10.1515/em-2022-0132","url":null,"abstract":"Abstract Objective To identify prognostic markers by applying machine learning strategies to the feature selection. Methods An observational, retrospective, multi-center study that included hospitalized patients with a confirmed diagnosis of COVID-19 in three hospitals in Colombia. Eight strategies were applied to select prognostic-related characteristics. Eight logistic regression models were built from each set of variables and the predictive ability of the outcome was evaluated. The primary endpoint was transfer to intensive care or in-hospital death. Results The database consisted of 969 patients of which 486 had complete data. The main outcome occurred in 169 cases. The development database included 220 patients, 137 (62.3%) were men with a median age of 58.2, 39 (17.7%) were diabetic, 62 (28.2%) had high blood pressure, and 32 (14.5%) had chronic lung disease. Thirty-three variables were identified as prognostic markers, and those selected most frequently were: LDH, PaO2/FIO2 ratio, CRP, age, neutrophil and lymphocyte counts, respiratory rate, oxygen saturation, ferritin, and HCO3. The eight logistic regressions developed were validated on 266 patients in whom similar results were reached (accuracy: 65.8–72.9%). Conclusions The combined use of strategies for selecting characteristics through machine learning techniques makes it possible to identify a broad set of prognostic markers in patients hospitalized for COVID-19 for death or hospitalization in intensive care.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86505772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michele Santacatterina, B. Burke, Mihili Gunaratne, W. Weintraub, M. Espeland, Adolfo Correa, DeAnna J. Friedman-Klabanoff, M. Gibbs, David M. Herrington, Kristen Miller, J. Sanders, A. Seals, D. Uschner, T. Wierzba, Morgana Mongraw-Chaffin
Abstract Objectives The prevalence and incidence of SARS-CoV-2, the virus which causes COVID-19, at any given time remains controversial, and is an essential piece in understanding the dynamics of the epidemic. Cross-sectional studies and single time point testing approaches continue to struggle with appropriate adjustment methods for the high false positive rates in low prevalence settings or high false negative rates in high prevalence settings, and post-hoc adjustment at the group level does not fully address this issue for incidence even at the population level. Methods In this study, we use seroprevalence as an illustrative example of the benefits of using a case definition using a combined parallel and serial testing framework to confirm antibody-positive status. In a simulation study, we show that our proposed approach reduces bias and improves positive and negative predictive value across the range of prevalence compared with cross-sectional testing even with gold standard tests and post-hoc adjustment. Using data from the North Carolina COVID-19 Community Research Partnership, we applied the proposed case definition to the estimation of SARS-CoV-2 seroprevalence and incidence early in the pandemic. Results The proposed approach is not always feasible given the cost and time required to administer repeated tests; however, it reduces bias in both low and high prevalence settings and addresses misclassification at the individual level. This approach can be applied to almost all testing contexts and platforms. Conclusions This systematic approach offers better estimation of both prevalence and incidence, which is important to improve understanding and facilitate controlling the pandemic.
{"title":"Using repeated antibody testing to minimize bias in estimates of prevalence and incidence of SARS-CoV-2 infection","authors":"Michele Santacatterina, B. Burke, Mihili Gunaratne, W. Weintraub, M. Espeland, Adolfo Correa, DeAnna J. Friedman-Klabanoff, M. Gibbs, David M. Herrington, Kristen Miller, J. Sanders, A. Seals, D. Uschner, T. Wierzba, Morgana Mongraw-Chaffin","doi":"10.1515/em-2023-0012","DOIUrl":"https://doi.org/10.1515/em-2023-0012","url":null,"abstract":"Abstract Objectives The prevalence and incidence of SARS-CoV-2, the virus which causes COVID-19, at any given time remains controversial, and is an essential piece in understanding the dynamics of the epidemic. Cross-sectional studies and single time point testing approaches continue to struggle with appropriate adjustment methods for the high false positive rates in low prevalence settings or high false negative rates in high prevalence settings, and post-hoc adjustment at the group level does not fully address this issue for incidence even at the population level. Methods In this study, we use seroprevalence as an illustrative example of the benefits of using a case definition using a combined parallel and serial testing framework to confirm antibody-positive status. In a simulation study, we show that our proposed approach reduces bias and improves positive and negative predictive value across the range of prevalence compared with cross-sectional testing even with gold standard tests and post-hoc adjustment. Using data from the North Carolina COVID-19 Community Research Partnership, we applied the proposed case definition to the estimation of SARS-CoV-2 seroprevalence and incidence early in the pandemic. Results The proposed approach is not always feasible given the cost and time required to administer repeated tests; however, it reduces bias in both low and high prevalence settings and addresses misclassification at the individual level. This approach can be applied to almost all testing contexts and platforms. Conclusions This systematic approach offers better estimation of both prevalence and incidence, which is important to improve understanding and facilitate controlling the pandemic.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"253 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83681676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laine E. Thomas, Steven M. Thomas, Fan Li, Roland A. Matsouaka
Abstract Objectives Propensity score (PS) weighting methods are commonly used to adjust for confounding in observational treatment comparisons. However, in the setting of substantial covariate imbalance, PS values may approach 0 and 1, yielding extreme weights and inflated variance of the estimated treatment effect. Adaptations of the standard inverse probability of treatment weights (IPTW) can reduce the influence of extremes, including trimming methods that exclude people with PS values near 0 or 1. Alternatively, overlap weighting (OW) optimizes criteria related to bias and variance, and performs well compared to other PS weighting and matching methods. However, it has not been compared to propensity score stratification (PSS). PSS has some of the same potential advantages; being insensitive extreme values. We sought to compare these methods in the setting of substantial covariate imbalance to generate practical recommendations. Methods Analytical derivations were used to establish connections between methods, and simulation studies were conducted to assess bias and variance of alternative methods. Results We find that OW is generally superior, particularly as covariate imbalance increases. In addition, a common method for implementing PSS based on Mantel–Haenszel weights (PSS-MH) is equivalent to a coarsened version of OW and can perform nearly as well. Finally, trimming methods increase bias across methods (IPTW, PSS and PSS-MH) unless the PS model is re-fit to the trimmed sample and weights or strata are re-derived. After trimming with re-fitting, all methods perform similarly to OW. Conclusions These results may guide the selection, implementation and reporting of PS methods for observational studies with substantial covariate imbalance.
{"title":"Addressing substantial covariate imbalance with propensity score stratification and balancing weights: connections and recommendations","authors":"Laine E. Thomas, Steven M. Thomas, Fan Li, Roland A. Matsouaka","doi":"10.1515/em-2022-0131","DOIUrl":"https://doi.org/10.1515/em-2022-0131","url":null,"abstract":"Abstract Objectives Propensity score (PS) weighting methods are commonly used to adjust for confounding in observational treatment comparisons. However, in the setting of substantial covariate imbalance, PS values may approach 0 and 1, yielding extreme weights and inflated variance of the estimated treatment effect. Adaptations of the standard inverse probability of treatment weights (IPTW) can reduce the influence of extremes, including trimming methods that exclude people with PS values near 0 or 1. Alternatively, overlap weighting (OW) optimizes criteria related to bias and variance, and performs well compared to other PS weighting and matching methods. However, it has not been compared to propensity score stratification (PSS). PSS has some of the same potential advantages; being insensitive extreme values. We sought to compare these methods in the setting of substantial covariate imbalance to generate practical recommendations. Methods Analytical derivations were used to establish connections between methods, and simulation studies were conducted to assess bias and variance of alternative methods. Results We find that OW is generally superior, particularly as covariate imbalance increases. In addition, a common method for implementing PSS based on Mantel–Haenszel weights (PSS-MH) is equivalent to a coarsened version of OW and can perform nearly as well. Finally, trimming methods increase bias across methods (IPTW, PSS and PSS-MH) unless the PS model is re-fit to the trimmed sample and weights or strata are re-derived. After trimming with re-fitting, all methods perform similarly to OW. Conclusions These results may guide the selection, implementation and reporting of PS methods for observational studies with substantial covariate imbalance.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135604264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sachin Kumar, S. Pal, Vijendra Pratap Singh, P. Jaiswal
Abstract Objectives The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time. Methods Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud. Results We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing. Conclusions After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.
{"title":"Performance evaluation of ResNet model for classification of tomato plant disease","authors":"Sachin Kumar, S. Pal, Vijendra Pratap Singh, P. Jaiswal","doi":"10.1515/em-2021-0044","DOIUrl":"https://doi.org/10.1515/em-2021-0044","url":null,"abstract":"Abstract Objectives The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time. Methods Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud. Results We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing. Conclusions After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74304998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Subhankar Chattopadhyay, D. Ghosh, Raju Maiti, Samarjit Das, A. Biswas, Bibhas Chakraborty
Abstract Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.
日病例数和日死亡人数的快速增长使得印度第二波COVID-19大流行比第一波更具致命性。在此期间，印度各地报告的感染和伤亡人数创下了纪录。德里和马哈拉施特拉邦是印度第二波疫情中受灾最严重的两个地区。为此，印度政府在此期间在各邦实施了严格的干预政策(“封锁”、“保持社交距离”和“疫苗接种”)，以阻止病毒的传播。本文的目的是进行中断时间序列(ITS)分析，以研究干预措施对日常病例和死亡的影响。方法采用14 d (COVID-19潜伏期)观察窗，收集德里和马哈拉施特拉邦干预点前后的每日数据。采用分段线性回归分析研究干预后的坡度，以及干预后是否有直接变化。我们还在分析中加入了反事实和延迟时间效应，以研究我们的ITS设计的意义。结果在这里，我们观察到干预后的趋势在每日病例和每日死亡人数上都具有统计学意义和负相关。我们还发现，在干预开始后，趋势没有立即变化，因此我们研究了一些延迟时间效应，这些效应显示了趋势的变化是如何随着时间的推移而发生的。从我们研究中的反事实中，我们可以了解如果不实施干预措施，COVID的情况会发生什么。我们通过在分析中探索ITS设计的所有可能成分，从统计上试图找出德里和马哈拉施特拉邦不同的COVID情景，以便提出一个可行的设计，以表明实施适当的干预政策对于应对这种可能具有各种高传染性变异的大流行的重要性。
{"title":"A study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis","authors":"Subhankar Chattopadhyay, D. Ghosh, Raju Maiti, Samarjit Das, A. Biswas, Bibhas Chakraborty","doi":"10.1515/em-2022-0113","DOIUrl":"https://doi.org/10.1515/em-2022-0113","url":null,"abstract":"Abstract Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88001062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sara Burcham, Yuki Liu, Ashley L. Merianos, Angelico Mendy
Abstract Objectives An important step in preparing data for statistical analysis is outlier detection and removal, yet no gold standard exists in current literature. The objective of this study is to identify the ideal decision test using the National Health and Nutrition Examination Survey (NHANES) 2017–2018 dietary data. Methods We conducted a secondary analysis of NHANES 24-h dietary recalls, considering the survey's multi-stage cluster design. Six outlier detection and removal strategies were assessed by evaluating the decision tests' impact on the Pearson's correlation coefficient among macronutrients. Furthermore, we assessed changes in the effect size estimates based on pre-defined sample sizes. The data were collected as part of the 2017–2018 24-h dietary recall among adult participants (N=4,893). Results Effect estimate changes for macronutrients varied from 6.5 % for protein to 39.3 % for alcohol across all decision tests. The largest proportion of outliers removed was 4.0 % in the large sample size, for the decision test, >2 standard deviations from the mean. The smallest sample size, particularly for alcohol analysis, was most affected by the six decision tests when compared to no decision test. Conclusions This study, the first to use 2017–2018 NHANES dietary data for outlier evaluation, emphasizes the importance of selecting an appropriate decision test considering factors such as statistical power, sample size, normality assumptions, the proportion of data removed, effect estimate changes, and the consistency of estimates across sample sizes. We recommend the use of non-parametric tests for non-normally distributed variables of interest.
{"title":"Outliers in nutrient intake data for U.S. adults: national health and nutrition examination survey 2017–2018","authors":"Sara Burcham, Yuki Liu, Ashley L. Merianos, Angelico Mendy","doi":"10.1515/em-2023-0018","DOIUrl":"https://doi.org/10.1515/em-2023-0018","url":null,"abstract":"Abstract Objectives An important step in preparing data for statistical analysis is outlier detection and removal, yet no gold standard exists in current literature. The objective of this study is to identify the ideal decision test using the National Health and Nutrition Examination Survey (NHANES) 2017–2018 dietary data. Methods We conducted a secondary analysis of NHANES 24-h dietary recalls, considering the survey's multi-stage cluster design. Six outlier detection and removal strategies were assessed by evaluating the decision tests' impact on the Pearson's correlation coefficient among macronutrients. Furthermore, we assessed changes in the effect size estimates based on pre-defined sample sizes. The data were collected as part of the 2017–2018 24-h dietary recall among adult participants (N=4,893). Results Effect estimate changes for macronutrients varied from 6.5 % for protein to 39.3 % for alcohol across all decision tests. The largest proportion of outliers removed was 4.0 % in the large sample size, for the decision test, >2 standard deviations from the mean. The smallest sample size, particularly for alcohol analysis, was most affected by the six decision tests when compared to no decision test. Conclusions This study, the first to use 2017–2018 NHANES dietary data for outlier evaluation, emphasizes the importance of selecting an appropriate decision test considering factors such as statistical power, sample size, normality assumptions, the proportion of data removed, effect estimate changes, and the consistency of estimates across sample sizes. We recommend the use of non-parametric tests for non-normally distributed variables of interest.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Objectives In late 2019, the novel coronavirus, known as COVID-19, emerged in Wuhan, China, and rapidly spread worldwide, including in Germany. To mitigate the pandemic’s impact, various strategies, including vaccination and non-pharmaceutical interventions, have been implemented. However, the emergence of new, highly infectious SARS-CoV-2 strains has become the primary driving force behind the disease’s spread. Mathematical models, such as deterministic compartmental models, are essential for estimating contagion rates in different scenarios and predicting the pandemic’s behavior. Methods In this study, we present a novel model that incorporates vaccination dynamics, the three most prevalent virus strains (wild-type, alpha, and delta), infected individuals’ detection status, and pre-symptomatic transmission to represent the pandemic’s course in Germany from March 2, 2020, to August 17, 2021. Results By analyzing the behavior of the German population over 534 days and 25 time intervals, we estimated various parameters, including transmission, recovery, mortality, and detection. Furthermore, we conducted an alternative analysis of vaccination scenarios under the same interval conditions, emphasizing the importance of vaccination administration and awareness. Conclusions Our 534-day analysis provides policymakers with a range of circumstances and parameters that can be used to simulate future scenarios. The proposed model can also be used to make predictions and inform policy decisions related to pandemic control in Germany and beyond.
{"title":"A compartmental model of the COVID-19 pandemic course in Germany","authors":"Yıldırım Adalıoğlu, Çağan Kaplan","doi":"10.1515/em-2022-0126","DOIUrl":"https://doi.org/10.1515/em-2022-0126","url":null,"abstract":"Abstract Objectives In late 2019, the novel coronavirus, known as COVID-19, emerged in Wuhan, China, and rapidly spread worldwide, including in Germany. To mitigate the pandemic’s impact, various strategies, including vaccination and non-pharmaceutical interventions, have been implemented. However, the emergence of new, highly infectious SARS-CoV-2 strains has become the primary driving force behind the disease’s spread. Mathematical models, such as deterministic compartmental models, are essential for estimating contagion rates in different scenarios and predicting the pandemic’s behavior. Methods In this study, we present a novel model that incorporates vaccination dynamics, the three most prevalent virus strains (wild-type, alpha, and delta), infected individuals’ detection status, and pre-symptomatic transmission to represent the pandemic’s course in Germany from March 2, 2020, to August 17, 2021. Results By analyzing the behavior of the German population over 534 days and 25 time intervals, we estimated various parameters, including transmission, recovery, mortality, and detection. Furthermore, we conducted an alternative analysis of vaccination scenarios under the same interval conditions, emphasizing the importance of vaccination administration and awareness. Conclusions Our 534-day analysis provides policymakers with a range of circumstances and parameters that can be used to simulate future scenarios. The proposed model can also be used to make predictions and inform policy decisions related to pandemic control in Germany and beyond.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84699830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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