Abstract In this paper an analysis of the first diffusion of the Covid-19 outbreak occurred in late February 2020 in Northern Italy is presented. In order to study the time evolution of the epidemic it was decided to analyze in particular as the most relevant variable the number of hospitalized people, considered as the less biased proxy of the real number of infected people. An approximate solution of the infected equation was found from a simplified version of the SIR model. This solution was used as a tool for the calculation of the basic reproduction number R 0 in the early phase of the epidemic for the most affected Northern Italian regions (Piedmont, Lombardy, Veneto and Emilia), giving values of R 0 ranging from 2.2 to 3.1. Finally, a theoretical formulation of the infection rate is proposed, introducing a new parameter, the infection length, characteristic of the disease.
{"title":"The first diffusion of the Covid-19 outbreak in Northern Italy: an analysis based on a simplified version of the SIR model","authors":"M. Magnoni","doi":"10.1515/em-2020-0047","DOIUrl":"https://doi.org/10.1515/em-2020-0047","url":null,"abstract":"Abstract In this paper an analysis of the first diffusion of the Covid-19 outbreak occurred in late February 2020 in Northern Italy is presented. In order to study the time evolution of the epidemic it was decided to analyze in particular as the most relevant variable the number of hospitalized people, considered as the less biased proxy of the real number of infected people. An approximate solution of the infected equation was found from a simplified version of the SIR model. This solution was used as a tool for the calculation of the basic reproduction number R 0 in the early phase of the epidemic for the most affected Northern Italian regions (Piedmont, Lombardy, Veneto and Emilia), giving values of R 0 ranging from 2.2 to 3.1. Finally, a theoretical formulation of the infection rate is proposed, introducing a new parameter, the infection length, characteristic of the disease.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77695767","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}
Kaare Græsbøll, L. Christiansen, U. H. Thygesen, C. Kirkeby
Abstract Objectives: Travel restrictions is an often-used tool for governments to prevent the spread of COVID-19. Methods: We here used a simple simulation model to investigate the potential effects of travel restrictions within a country. Results: We found that travel restrictions can delay the peak of the epidemic considerably, but do not affect the spread within the country. We also investigated the effect of implementing travel restrictions early or later in the epidemic, and found that fast implementation is crucial for delaying the epidemic. Conclusions: Fast implementation of travel restrictions is crucial for delaying the peak of a subsequent outbreak of COVID-19 within a country.
{"title":"Delaying the peak of the COVID-19 epidemic with travel restrictions","authors":"Kaare Græsbøll, L. Christiansen, U. H. Thygesen, C. Kirkeby","doi":"10.1515/em-2020-0042","DOIUrl":"https://doi.org/10.1515/em-2020-0042","url":null,"abstract":"Abstract Objectives: Travel restrictions is an often-used tool for governments to prevent the spread of COVID-19. Methods: We here used a simple simulation model to investigate the potential effects of travel restrictions within a country. Results: We found that travel restrictions can delay the peak of the epidemic considerably, but do not affect the spread within the country. We also investigated the effect of implementing travel restrictions early or later in the epidemic, and found that fast implementation is crucial for delaying the epidemic. Conclusions: Fast implementation of travel restrictions is crucial for delaying the peak of a subsequent outbreak of COVID-19 within a country.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87018220","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 A curfew was introduced in France in October 2020 to reduce the spread of Covid-19. This was done for two weeks in 16 departments, or for one week in 38 others, 42 departments not being subjected to the curfew. This article compares the number of new daily hospital admissions in these departments. Methods The ratio of the number of new hospitalisations during these two weeks and in the previous two weeks was computed in the three categories of departments. Results The increase in new hospitalisations was lower in departments under curfew for two weeks than in all other departments, and this result does not seem to be linked to characteristics of the departments before curfew. Conclusions This result shows that the two-week curfew is linked to a lower increase of hospitalisations, but not that the curfew by itself is the cause of this result, as other factors may have played a role.
{"title":"Covid-19: were curfews in France associated with hospitalisations?","authors":"É. Le Bourg","doi":"10.1515/em-2021-0011","DOIUrl":"https://doi.org/10.1515/em-2021-0011","url":null,"abstract":"Abstract Objectives A curfew was introduced in France in October 2020 to reduce the spread of Covid-19. This was done for two weeks in 16 departments, or for one week in 38 others, 42 departments not being subjected to the curfew. This article compares the number of new daily hospital admissions in these departments. Methods The ratio of the number of new hospitalisations during these two weeks and in the previous two weeks was computed in the three categories of departments. Results The increase in new hospitalisations was lower in departments under curfew for two weeks than in all other departments, and this result does not seem to be linked to characteristics of the departments before curfew. Conclusions This result shows that the two-week curfew is linked to a lower increase of hospitalisations, but not that the curfew by itself is the cause of this result, as other factors may have played a role.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86907559","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}
L. A. Bautista Balbás, M. Gil Conesa, Blanca Bautista Balbás, G. Rodríguez Caravaca
Abstract Objectives An essential indicator of COVID-19 transmission is the effective reproduction number (R t ), the number of cases which an infected individual is expected to infect at a particular point in time; curves of the evolution of R t over time (transmission curves) reflect the impact of preventive measures and whether an epidemic is controlled. Methods We have created a Shiny/R web application (https://alfredob.shinyapps.io/estR0/) with user-selectable features: open data sources with daily COVID-19 incidences from all countries and many regions, customizable preprocessing options (smoothing, proportional increment, etc.), different MonteCarlo-Markov-Chain estimates of the generation time or serial interval distributions and state-of-the-art R t estimation frameworks (EpiEstim, R 0). This application could be used as a tool both to obtain transmission estimates and to perform interactive sensitivity analysis. We have analyzed the impact of these factors on transmission curves. We also have obtained curves at the national and sub-national level and analyzed the impact of epidemic control strategies, superspreading events, socioeconomic factors and outbreaks. Results Reproduction numbers showed earlier anticipation compared to active prevalence indicators (14-day cumulative incidence, overall infectivity). In the sensitivity analysis, the impact of different R t estimation methods was moderate/small, and the impact of different serial interval distributions was very small. We couldn’t find conclusive evidence regarding the impact of alleged superspreading events. As a limitation, dataset quality can limit the reliability of the estimates. Conclusions The thorough review of many examples of COVID-19 transmission curves support the usage of R t estimates as a robust transmission indicator.
{"title":"COVID-19 effective reproduction number determination: an application, and a review of issues and influential factors","authors":"L. A. Bautista Balbás, M. Gil Conesa, Blanca Bautista Balbás, G. Rodríguez Caravaca","doi":"10.1515/em-2020-0048","DOIUrl":"https://doi.org/10.1515/em-2020-0048","url":null,"abstract":"Abstract Objectives An essential indicator of COVID-19 transmission is the effective reproduction number (R t ), the number of cases which an infected individual is expected to infect at a particular point in time; curves of the evolution of R t over time (transmission curves) reflect the impact of preventive measures and whether an epidemic is controlled. Methods We have created a Shiny/R web application (https://alfredob.shinyapps.io/estR0/) with user-selectable features: open data sources with daily COVID-19 incidences from all countries and many regions, customizable preprocessing options (smoothing, proportional increment, etc.), different MonteCarlo-Markov-Chain estimates of the generation time or serial interval distributions and state-of-the-art R t estimation frameworks (EpiEstim, R 0). This application could be used as a tool both to obtain transmission estimates and to perform interactive sensitivity analysis. We have analyzed the impact of these factors on transmission curves. We also have obtained curves at the national and sub-national level and analyzed the impact of epidemic control strategies, superspreading events, socioeconomic factors and outbreaks. Results Reproduction numbers showed earlier anticipation compared to active prevalence indicators (14-day cumulative incidence, overall infectivity). In the sensitivity analysis, the impact of different R t estimation methods was moderate/small, and the impact of different serial interval distributions was very small. We couldn’t find conclusive evidence regarding the impact of alleged superspreading events. As a limitation, dataset quality can limit the reliability of the estimates. Conclusions The thorough review of many examples of COVID-19 transmission curves support the usage of R t estimates as a robust transmission indicator.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91155813","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}
S. Gounane, Y. Barkouch, Abdelghafour Atlas, M. Bendahmane, Fahd Karami, D. Meskine
Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.
近年来，人们提出了各种数学模型来模拟COVID-19的爆发。这些模型是研究新冠病毒传播机制和预测新冠病毒未来病程的有效工具。它们还用于评估控制这一流行病的战略。一般来说，SIR区室模型适用于理解和预测COVID-19等传染病的动态。经典SIR模型最初是由Kermack和McKendrick(参见Anderson, R. M. 1991)提出的。"讨论:Kermack-McKendrick流行病阈值定理"数学生物学通报53 (1):3-32;柯马克，W. O.和A. G.麦肯德里克。1927. 《流行病数学理论的贡献》《英国皇家学会学报》115(772):700-21)描述了易感、感染和恢复的隔室的演变。针对旨在遏制疫情的公共政策的影响，我们建立了一个新的非线性SIR流行病问题，该问题模拟了政府阻止冠状病毒传播措施引起的社会距离效应下冠状病毒的传播。为了找到每个国家(例如德国、西班牙、意大利、法国、阿尔及利亚和摩洛哥)采用的参数，我们根据实际的真实数据拟合提出的模型。我们还根据疫情的演变评估每个国家的政府措施。我们的数值模拟可以为预测疾病的传播提供有效的工具。
{"title":"An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting","authors":"S. Gounane, Y. Barkouch, Abdelghafour Atlas, M. Bendahmane, Fahd Karami, D. Meskine","doi":"10.1515/em-2020-0044","DOIUrl":"https://doi.org/10.1515/em-2020-0044","url":null,"abstract":"Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"48 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/em-2020-0044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72405578","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}
Pub Date : 2021-02-01DOI: 10.1101/2021.03.24.21253599
A. Anand, Saurabh Kumar, P. Ghosh
Abstract Objectives In recent times, researchers have used Susceptible-Infected-Susceptible (SIS) model to understand the spread of the COVID-19 pandemic. The SIS model has two compartments, susceptible and infected. In this model, the interest is to determine the number of infected cases at a given time point. However, it is also essential to know the cumulative number of infected cases at a given time point, which is not directly available from the SIS model's present structure. The objective is to provide a modified SIS model to address that gap. Methods In this work, we propose a modified structure of the SIS model to determine the cumulative number of infected cases at a given time point. We develop a dynamic data-driven algorithm to estimate the model parameters based on an optimally chosen training phase to predict the number of cumulative infected cases. Results We demonstrate the proposed algorithm's prediction performance using COVID-19 data from Delhi, India's capital city. Considering different time periods, we observed the proposed algorithm’s performance using the modified SIS model is well to predict the cumulative infected cases with two different prediction periods 30 and 40. Our study supports the idea of estimating the modified SIS model's parameters based on the optimal training phase instead of the entire history as the training phase. Conclusions Here, we have provided a modified SIS model that accounts for deaths due to disease and predicts cumulative infected cases based on an optimally chosen training phase. The proposed estimation process is beneficial when the disease under study changes its spreading pattern over time. We have developed the modified SIS model considering COVID-19 as the disease under focus. However, the model and algorithms can be applied to predict the cumulative cases of other infectious diseases.
{"title":"Dynamic data-driven algorithm to predict cumulative COVID-19 infected cases using susceptible-infected-susceptible model","authors":"A. Anand, Saurabh Kumar, P. Ghosh","doi":"10.1101/2021.03.24.21253599","DOIUrl":"https://doi.org/10.1101/2021.03.24.21253599","url":null,"abstract":"Abstract Objectives In recent times, researchers have used Susceptible-Infected-Susceptible (SIS) model to understand the spread of the COVID-19 pandemic. The SIS model has two compartments, susceptible and infected. In this model, the interest is to determine the number of infected cases at a given time point. However, it is also essential to know the cumulative number of infected cases at a given time point, which is not directly available from the SIS model's present structure. The objective is to provide a modified SIS model to address that gap. Methods In this work, we propose a modified structure of the SIS model to determine the cumulative number of infected cases at a given time point. We develop a dynamic data-driven algorithm to estimate the model parameters based on an optimally chosen training phase to predict the number of cumulative infected cases. Results We demonstrate the proposed algorithm's prediction performance using COVID-19 data from Delhi, India's capital city. Considering different time periods, we observed the proposed algorithm’s performance using the modified SIS model is well to predict the cumulative infected cases with two different prediction periods 30 and 40. Our study supports the idea of estimating the modified SIS model's parameters based on the optimal training phase instead of the entire history as the training phase. Conclusions Here, we have provided a modified SIS model that accounts for deaths due to disease and predicts cumulative infected cases based on an optimally chosen training phase. The proposed estimation process is beneficial when the disease under study changes its spreading pattern over time. We have developed the modified SIS model considering COVID-19 as the disease under focus. However, the model and algorithms can be applied to predict the cumulative cases of other infectious diseases.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88157708","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}
Pub Date : 2021-01-06DOI: 10.1101/2021.01.06.425544
Xia Wang, Dorcas Washington, G. Weber
Abstract Objectives The non-linear progression of new infection numbers in a pandemic poses challenges to the evaluation of its management. The tools of complex systems research may aid in attaining information that would be difficult to extract with other means. Methods To study the COVID-19 pandemic, we utilize the reported new cases per day for the globe, nine countries and six US states through October 2020. Fourier and univariate wavelet analyses inform on periodicity and extent of change. Results Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections. Bivariate wavelet analysis and return plots give indications of containment vs. exacerbation. Homogeneity or heterogeneity in the population response, uptick vs. suppression, and worsening or improving trends are discernible, in part by plotting various time lags in three dimensions. Conclusions The analysis of epidemic or pandemic progression with the techniques available for observed (noisy) complex data can extract important characteristics and aid decision making in the public health response.
{"title":"Complex systems analysis informs on the spread of COVID-19","authors":"Xia Wang, Dorcas Washington, G. Weber","doi":"10.1101/2021.01.06.425544","DOIUrl":"https://doi.org/10.1101/2021.01.06.425544","url":null,"abstract":"Abstract Objectives The non-linear progression of new infection numbers in a pandemic poses challenges to the evaluation of its management. The tools of complex systems research may aid in attaining information that would be difficult to extract with other means. Methods To study the COVID-19 pandemic, we utilize the reported new cases per day for the globe, nine countries and six US states through October 2020. Fourier and univariate wavelet analyses inform on periodicity and extent of change. Results Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections. Bivariate wavelet analysis and return plots give indications of containment vs. exacerbation. Homogeneity or heterogeneity in the population response, uptick vs. suppression, and worsening or improving trends are discernible, in part by plotting various time lags in three dimensions. Conclusions The analysis of epidemic or pandemic progression with the techniques available for observed (noisy) complex data can extract important characteristics and aid decision making in the public health response.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83280331","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}
E. Janssen, Michael Vuolo, Clément Gérome, A. Cadet-Taïrou
Abstract This article presents original mixed method research to describe the use of rare illicit psychoactive substances, with special emphasis on crack cocaine in France. We first introduce a unique monitoring system committed to the observation of hard-to-reach populations. Qualitative findings rely, among others, on perennial ethnographic studies and field professionals’ knowledge to provide guidance to estimate the number of crack cocaine users. We then rely on a set of multilevel capture-recapture estimators, a statistical procedure to indirectly estimate the size of elusive populations. Since prior field evidence suggests an increasing diversity in crack cocaine users’ profiles, we provide a measure of heterogeneity to assess which estimator better fits the data. The calculated estimates are then critically reviewed and debated in light of the previously gathered information. Our results uncover both individual and institutional heterogeneity and suggest that the spread of crack cocaine in France initiated earlier than originally thought. Our case study underlines the need for field-driven assessments to put quantitative results into perspective, a necessary step to tailor efficient health policy responses.
{"title":"Mixed methods to assess the use of rare illicit psychoactive substances: a case study","authors":"E. Janssen, Michael Vuolo, Clément Gérome, A. Cadet-Taïrou","doi":"10.1515/em-2020-0031","DOIUrl":"https://doi.org/10.1515/em-2020-0031","url":null,"abstract":"Abstract This article presents original mixed method research to describe the use of rare illicit psychoactive substances, with special emphasis on crack cocaine in France. We first introduce a unique monitoring system committed to the observation of hard-to-reach populations. Qualitative findings rely, among others, on perennial ethnographic studies and field professionals’ knowledge to provide guidance to estimate the number of crack cocaine users. We then rely on a set of multilevel capture-recapture estimators, a statistical procedure to indirectly estimate the size of elusive populations. Since prior field evidence suggests an increasing diversity in crack cocaine users’ profiles, we provide a measure of heterogeneity to assess which estimator better fits the data. The calculated estimates are then critically reviewed and debated in light of the previously gathered information. Our results uncover both individual and institutional heterogeneity and suggest that the spread of crack cocaine in France initiated earlier than originally thought. Our case study underlines the need for field-driven assessments to put quantitative results into perspective, a necessary step to tailor efficient health policy responses.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78309946","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 We introduce a simple and unified methodology to estimate the bias of Pearson correlation coefficients, partial correlation coefficients, and semi-partial correlation coefficients. Methods Our methodology features non-parametric bootstrapping and can accommodate small sample data without making any distributional assumptions. Results Two examples with R code are provided to illustrate the computation. Conclusions The computation strategy is easy to implement and remains the same, be it Pearson correlation or partial or semi-partial correlation.
{"title":"A simplified approach to bias estimation for correlations","authors":"X. Liu","doi":"10.1515/em-2021-0015","DOIUrl":"https://doi.org/10.1515/em-2021-0015","url":null,"abstract":"Abstract Objectives We introduce a simple and unified methodology to estimate the bias of Pearson correlation coefficients, partial correlation coefficients, and semi-partial correlation coefficients. Methods Our methodology features non-parametric bootstrapping and can accommodate small sample data without making any distributional assumptions. Results Two examples with R code are provided to illustrate the computation. Conclusions The computation strategy is easy to implement and remains the same, be it Pearson correlation or partial or semi-partial correlation.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"212 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76184475","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}
T. B. Sørensen, S. Vansteelandt, R. Wilson, J. Gregson, B. Shankar, S. Kinra, A. Dangour
Abstract Objectives: The current study aims to estimate the causal effect of increasing levels of urbanisation on mean SBP, and to decompose the direct and indirect effects via hypothesised mediators. Methods: We analysed data from 5, 840 adults (≥ 18 years) from the Andhra Pradesh Children and Parents study (APCAPS) conducted in 27 villages in Telangana, South India. The villages experienced different amounts of urbanisation during preceding decades and ranged from a rural village to a medium sized town. We estimated urbanisation levels of surveyed villages by combining remote sensing data of night-time light intensity (NTLI), measured by unitless digital numbers, with satellite imagery and ground surveying of village boundaries. We performed mediation analysis using linear mixed-effects models with SBP as the outcome, log-transformed continuous NTLI as the exposure, and three composite mediators summarising information on (i) socio-demographics (e.g., occupation and education); (ii) lifestyle and mental health (e.g., diet and depression); (iii) metabolic factors (e.g., fasting glucose and triglycerides). All models fitted random intercepts to account for clustering by villages and households and adjusted for confounders. Results: The NTLI range across the 27 villages was 62 to 1081 (4.1 to 7.0 on the log scale). Mean SBP was 122.7 mmHg (±15.7) among men and 115.8 mmHg (±14.2) among women. One unit (integer) log-NTLI increase was associated with a rise in mean SBP of 2.1 mmHg (95% CI 0.6, 3.5) among men and 1.3 mmHg (95% CI 0.0, 2.6) among women. We identified a positive indirect effect of log-NTLI on SBP via the metabolic pathway, where one log-NTLI increase elevated SBP by 4.6 mmHg (95% CI 2.0, 7.3) among men and by 0.7 mmHg (95% 0.1, 1.3) among women. There was a positive indirect effect of log-NTLI on SBP via the lifestyle and mental health pathway among men, where one log-NTLI increase elevated SBP by 0.7 mmHg (95% CI 0.1, 1.3). Observed negative direct effects of log-NTLI on SBP and positive indirect effects via the socio-demographic pathway among both genders; as well as a positive indirect effect via the lifestyle and mental health pathway among women, were not statistically significant at the 5% level. The sizes of effects were approximately doubled among participants ≥40 years of age. Conclusion: Our findings offer new insights into the pathways via which urbanisation level may act on blood pressure. Large indirect effects via metabolic factors, independent of socio-demographic, lifestyle and mental health factors identify a need to understand better the indirect effects of environmental cardiovascular disease (CVD) risk factors that change with urbanisation. We encourage researchers to use causal methods in further quantification of path-specific effects of place of residence on CVDs and risk factors. Available evidence-based, cost-effective interventions that target upstream determinants of CVDs should be implemented across all socio
摘要目的:本研究旨在估计城市化水平提高对平均收缩压的因果关系，并通过假设的介质分解直接和间接影响。方法:我们分析了来自印度南部泰伦加纳27个村庄的安得拉邦儿童和父母研究(APCAPS)的5840名成年人(≥18岁)的数据。在过去的几十年里，这些村庄经历了不同程度的城市化，从一个农村到一个中等规模的城镇。我们将夜间光强(NTLI)遥感数据(以无单位数字测量)与卫星图像和村庄边界的地面测量相结合，估计了被调查村庄的城市化水平。我们使用线性混合效应模型进行了中介分析，其中SBP为结果，对数转换的连续NTLI为暴露，三个复合中介总结了以下信息:(1)社会人口统计学(如职业和教育);(二)生活方式和心理健康(如饮食和抑郁症);(iii)代谢因素(如空腹血糖和甘油三酯)。所有模型都拟合随机截距，以解释村庄和家庭的聚类，并根据混杂因素进行调整。结果:27个村庄的nhti指数范围为62 ~ 1081(对数尺度4.1 ~ 7.0)。男性平均收缩压为122.7 mmHg(±15.7)，女性为115.8 mmHg(±14.2)。一个单位(整数)log-NTLI增加与男性平均收缩压升高2.1 mmHg (95% CI 0.6, 3.5)和女性平均收缩压升高1.3 mmHg (95% CI 0.0, 2.6)相关。我们发现log-NTLI通过代谢途径对收缩压有积极的间接影响，其中一个log-NTLI在男性中使升高的收缩压增加4.6 mmHg (95% CI 2.0, 7.3)，在女性中增加0.7 mmHg (95% CI 0.1, 1.3)。在男性中，log-NTLI通过生活方式和心理健康途径对收缩压有积极的间接影响，其中一个log-NTLI使升高的收缩压增加0.7 mmHg (95% CI 0.1, 1.3)。观察到log-NTLI对收缩压的直接负作用和通过社会人口统计学途径的间接正作用;此外，通过生活方式和心理健康途径对女性产生的积极间接影响，在5%的水平上没有统计学意义。在年龄≥40岁的参与者中，影响的大小大约增加了一倍。结论:我们的研究结果为城市化水平对血压的影响途径提供了新的见解。独立于社会人口、生活方式和心理健康因素的代谢因素产生的巨大间接影响表明，有必要更好地了解随着城市化而变化的环境心血管疾病(CVD)风险因素的间接影响。我们鼓励研究人员使用因果方法进一步量化居住地对心血管疾病和危险因素的路径特异性影响。针对心血管疾病上游决定因素的现有循证、具有成本效益的干预措施应在印度所有社会人口梯度中实施。
{"title":"Quantifying the influence of location of residence on blood pressure in urbanising South India: a path analysis with multiple mediators","authors":"T. B. Sørensen, S. Vansteelandt, R. Wilson, J. Gregson, B. Shankar, S. Kinra, A. Dangour","doi":"10.1515/em-2019-0035","DOIUrl":"https://doi.org/10.1515/em-2019-0035","url":null,"abstract":"Abstract Objectives: The current study aims to estimate the causal effect of increasing levels of urbanisation on mean SBP, and to decompose the direct and indirect effects via hypothesised mediators. Methods: We analysed data from 5, 840 adults (≥ 18 years) from the Andhra Pradesh Children and Parents study (APCAPS) conducted in 27 villages in Telangana, South India. The villages experienced different amounts of urbanisation during preceding decades and ranged from a rural village to a medium sized town. We estimated urbanisation levels of surveyed villages by combining remote sensing data of night-time light intensity (NTLI), measured by unitless digital numbers, with satellite imagery and ground surveying of village boundaries. We performed mediation analysis using linear mixed-effects models with SBP as the outcome, log-transformed continuous NTLI as the exposure, and three composite mediators summarising information on (i) socio-demographics (e.g., occupation and education); (ii) lifestyle and mental health (e.g., diet and depression); (iii) metabolic factors (e.g., fasting glucose and triglycerides). All models fitted random intercepts to account for clustering by villages and households and adjusted for confounders. Results: The NTLI range across the 27 villages was 62 to 1081 (4.1 to 7.0 on the log scale). Mean SBP was 122.7 mmHg (±15.7) among men and 115.8 mmHg (±14.2) among women. One unit (integer) log-NTLI increase was associated with a rise in mean SBP of 2.1 mmHg (95% CI 0.6, 3.5) among men and 1.3 mmHg (95% CI 0.0, 2.6) among women. We identified a positive indirect effect of log-NTLI on SBP via the metabolic pathway, where one log-NTLI increase elevated SBP by 4.6 mmHg (95% CI 2.0, 7.3) among men and by 0.7 mmHg (95% 0.1, 1.3) among women. There was a positive indirect effect of log-NTLI on SBP via the lifestyle and mental health pathway among men, where one log-NTLI increase elevated SBP by 0.7 mmHg (95% CI 0.1, 1.3). Observed negative direct effects of log-NTLI on SBP and positive indirect effects via the socio-demographic pathway among both genders; as well as a positive indirect effect via the lifestyle and mental health pathway among women, were not statistically significant at the 5% level. The sizes of effects were approximately doubled among participants ≥40 years of age. Conclusion: Our findings offer new insights into the pathways via which urbanisation level may act on blood pressure. Large indirect effects via metabolic factors, independent of socio-demographic, lifestyle and mental health factors identify a need to understand better the indirect effects of environmental cardiovascular disease (CVD) risk factors that change with urbanisation. We encourage researchers to use causal methods in further quantification of path-specific effects of place of residence on CVDs and risk factors. Available evidence-based, cost-effective interventions that target upstream determinants of CVDs should be implemented across all socio","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87915187","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：