Mamadou Yauck, E. Moodie, Herak Apelian, Marc-Messier Peet, G. Lambert, D. Grace, N. Lachowsky, T. Hart, J. Cox
Abstract Objectives Respondent-Driven Sampling (RDS) is a variant of link-tracing, a sampling technique for surveying hard-to-reach communities that takes advantage of community members' social networks to reach potential participants. While the RDS sampling mechanism and associated methods of adjusting for the sampling at the analysis stage are well-documented in the statistical sciences literature, methodological focus has largely been restricted to estimation of population means and proportions, while giving little to no consideration to the estimation of population network parameters. As a network-based sampling method, RDS is faced with the fundamental problem of sampling from population networks where features such as homophily (the tendency for individuals with similar traits to share social ties) and differential activity (the ratio of the average number of connections by attribute) are sensitive to the choice of a sampling method. Methods Many simple approaches exist to generate simulated RDS data, with specific levels of network features (mainly homophily and differential activity), where the focus is on estimating means and proportions (Gile 2011; Gile et al. 2015; Spiller et al. 2018). However, recent findings on the inconsistency of estimators of network features such as homophily in partially observed networks (Crawford et al. 2017; Shalizi and Rinaldo 2013) raise the question of whether those target features can be recovered using the observed RDS data alone – as recovering information about these features is critical if we wish to condition upon them. In this paper, we conduct a simulation study to assess the accuracy of existing RDS simulation methods, in terms of their abilities to generate RDS samples with the desired levels of two network parameters: homophily and differential activity. Results The results show that (1) homophily cannot be consistently estimated from simulated RDS samples and (2) differential activity estimators are more precise when groups, defined by traits, are equally active and equally represented in the population. We use this approach to mimic features of the Engage Study, an RDS sample of gay, bisexual and other men who have sex with men in Montréal, Canada. Conclusions In this paper, we highlight that it is possible, in some cases, to simulate population networks by mimicking the characteristics of real-world RDS data while retaining accuracy and precision for target network features in the samples.
被调查者驱动抽样(RDS)是链接追踪的一种变体，是一种利用社区成员的社会网络来接触潜在参与者的抽样技术，用于调查难以到达的社区。虽然RDS抽样机制和在分析阶段调整抽样的相关方法在统计科学文献中有充分的记载，但方法重点在很大程度上局限于估计人口均值和比例，而很少或根本没有考虑估计人口网络参数。作为一种基于网络的抽样方法，RDS面临着从人口网络中抽样的基本问题，其中同质性(具有相似特征的个体共享社会关系的趋势)和差异活动(按属性划分的平均连接数的比率)等特征对抽样方法的选择很敏感。存在许多简单的方法来生成具有特定级别网络特征(主要是同质性和差异活动)的模拟RDS数据，其中重点是估计均值和比例(Gile 2011;Gile et al. 2015;Spiller et al. 2018)。然而，最近关于网络特征(如部分观察到的网络中的同态)估计量的不一致性的发现(Crawford et al. 2017;Shalizi和Rinaldo(2013)提出了一个问题，即是否可以单独使用观察到的RDS数据来恢复这些目标特征——因为如果我们希望以这些特征为条件，恢复有关这些特征的信息是至关重要的。在本文中，我们进行了一项模拟研究，以评估现有RDS模拟方法的准确性，根据它们生成具有两个网络参数(同质性和差分活性)所需水平的RDS样本的能力。结果表明:(1)从模拟RDS样本中无法一致地估计出同质性;(2)当由特征定义的群体在总体中具有同等的活跃度和代表性时，差分活动估计更为精确。我们使用这种方法来模仿Engage研究的特征，这是一项RDS样本，研究对象是加拿大montrsamal的同性恋、双性恋和其他与男性发生性关系的男性。在本文中，我们强调，在某些情况下，通过模仿现实世界RDS数据的特征来模拟人口网络，同时保留样本中目标网络特征的准确性和精度是可能的。
{"title":"Sampling from networks: respondent-driven sampling","authors":"Mamadou Yauck, E. Moodie, Herak Apelian, Marc-Messier Peet, G. Lambert, D. Grace, N. Lachowsky, T. Hart, J. Cox","doi":"10.1515/em-2020-0033","DOIUrl":"https://doi.org/10.1515/em-2020-0033","url":null,"abstract":"Abstract Objectives Respondent-Driven Sampling (RDS) is a variant of link-tracing, a sampling technique for surveying hard-to-reach communities that takes advantage of community members' social networks to reach potential participants. While the RDS sampling mechanism and associated methods of adjusting for the sampling at the analysis stage are well-documented in the statistical sciences literature, methodological focus has largely been restricted to estimation of population means and proportions, while giving little to no consideration to the estimation of population network parameters. As a network-based sampling method, RDS is faced with the fundamental problem of sampling from population networks where features such as homophily (the tendency for individuals with similar traits to share social ties) and differential activity (the ratio of the average number of connections by attribute) are sensitive to the choice of a sampling method. Methods Many simple approaches exist to generate simulated RDS data, with specific levels of network features (mainly homophily and differential activity), where the focus is on estimating means and proportions (Gile 2011; Gile et al. 2015; Spiller et al. 2018). However, recent findings on the inconsistency of estimators of network features such as homophily in partially observed networks (Crawford et al. 2017; Shalizi and Rinaldo 2013) raise the question of whether those target features can be recovered using the observed RDS data alone – as recovering information about these features is critical if we wish to condition upon them. In this paper, we conduct a simulation study to assess the accuracy of existing RDS simulation methods, in terms of their abilities to generate RDS samples with the desired levels of two network parameters: homophily and differential activity. Results The results show that (1) homophily cannot be consistently estimated from simulated RDS samples and (2) differential activity estimators are more precise when groups, defined by traits, are equally active and equally represented in the population. We use this approach to mimic features of the Engage Study, an RDS sample of gay, bisexual and other men who have sex with men in Montréal, Canada. Conclusions In this paper, we highlight that it is possible, in some cases, to simulate population networks by mimicking the characteristics of real-world RDS data while retaining accuracy and precision for target network features in the samples.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80296162","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}
Helena Baptista, Peter Congdon, J. Mendes, A. Rodrigues, H. Canhão, S. Dias
Abstract Recent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model. The methods are specially design to handle cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Both approaches proposed in this article are producing results consistent with the published knowledge, and are increasing the accuracy to clearly determine areas of high- or low-risk.
{"title":"Disease mapping models for data with weak spatial dependence or spatial discontinuities","authors":"Helena Baptista, Peter Congdon, J. Mendes, A. Rodrigues, H. Canhão, S. Dias","doi":"10.1515/em-2019-0025","DOIUrl":"https://doi.org/10.1515/em-2019-0025","url":null,"abstract":"Abstract Recent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model. The methods are specially design to handle cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Both approaches proposed in this article are producing results consistent with the published knowledge, and are increasing the accuracy to clearly determine areas of high- or low-risk.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74224894","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 Within the context of multiple regression the coefficient of determination can be converted to a probability of agreement between the actual and predicted outcomes, suitably dichotomized. Methods This probability of agreement can be used as a simple index of prediction accuracy to help capture the probability of a correct prediction in multiple regression. Results The simple index of prediction accuracy makes the multiple correlation comprehensible to statisticians and laypeople alike. Two examples are provided to demonstrate the application of the simple index. Conclusions In short, the paper introduces the simple index, its computation formula, and its theoretical affinity to the confusion matrix, binomial effect size, probit model, and tetrachoric correlation.
{"title":"A simple index of prediction accuracy in multiple regression analysis","authors":"X. Liu","doi":"10.1515/em-2020-0028","DOIUrl":"https://doi.org/10.1515/em-2020-0028","url":null,"abstract":"Abstract Objectives Within the context of multiple regression the coefficient of determination can be converted to a probability of agreement between the actual and predicted outcomes, suitably dichotomized. Methods This probability of agreement can be used as a simple index of prediction accuracy to help capture the probability of a correct prediction in multiple regression. Results The simple index of prediction accuracy makes the multiple correlation comprehensible to statisticians and laypeople alike. Two examples are provided to demonstrate the application of the simple index. Conclusions In short, the paper introduces the simple index, its computation formula, and its theoretical affinity to the confusion matrix, binomial effect size, probit model, and tetrachoric correlation.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76806643","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}
Linda Abrahamsson, Maya Alsheh Ali, K. Czene, G. Isheden, P. Hall, K. Humphreys
Abstract Introduction Percentage mammographic density has long been recognised as a marker of breast cancer risk and of mammography sensitivity. There may be other image markers of screening sensitivity and efficient statistical approaches would be helpful for establishing them from large scale epidemiological and screening data. Methods We compare a novel random effects continuous tumour growth model (which includes a screening sensitivity submodel) to logistic regression (with interval vs. screen-detected cancer as the dependent variable) in terms of statistical power to detect image markers of screening sensitivity. We do this by carrying out a simulation study. We also use continuous tumour growth modelling to quantify the roles of dense tissue scatter (measured as skewness of the intensity gradient) and percentage mammographic density in screening sensitivity. This is done by using mammograms and information on tumour size, mode of detection and screening history from 1,845 postmenopausal women diagnosed with invasive breast cancer, in Sweden between 1993 and 1995. Results The statistical power to detect a marker of screening sensitivity was larger for our continuous tumour growth model than it was for logistic regression. For the settings considered in this paper, the percentage increase in power ranged from 34 to 56%. In our analysis of data from Swedish breast cancer patients, using our continuous growth model, when including both percentage mammographic density and dense tissue scatter in the screening sensitivity submodel, only the latter variable was significantly associated with sensitivity. When included one at a time, both markers were significantly associated (p-values of 5.7 × 10−3 and 1.0 × 10−5 for percentage mammographic density and dense tissue scatter, respectively). Conclusions Our continuous tumour growth model is useful for finding image markers of screening sensitivity and for quantifying their role, using large scale epidemiological and screening data. Clustered dense tissue is associated with low mammography screening sensitivity.
{"title":"Random effects tumour growth models for identifying image markers of mammography screening sensitivity","authors":"Linda Abrahamsson, Maya Alsheh Ali, K. Czene, G. Isheden, P. Hall, K. Humphreys","doi":"10.1515/em-2019-0022","DOIUrl":"https://doi.org/10.1515/em-2019-0022","url":null,"abstract":"Abstract Introduction Percentage mammographic density has long been recognised as a marker of breast cancer risk and of mammography sensitivity. There may be other image markers of screening sensitivity and efficient statistical approaches would be helpful for establishing them from large scale epidemiological and screening data. Methods We compare a novel random effects continuous tumour growth model (which includes a screening sensitivity submodel) to logistic regression (with interval vs. screen-detected cancer as the dependent variable) in terms of statistical power to detect image markers of screening sensitivity. We do this by carrying out a simulation study. We also use continuous tumour growth modelling to quantify the roles of dense tissue scatter (measured as skewness of the intensity gradient) and percentage mammographic density in screening sensitivity. This is done by using mammograms and information on tumour size, mode of detection and screening history from 1,845 postmenopausal women diagnosed with invasive breast cancer, in Sweden between 1993 and 1995. Results The statistical power to detect a marker of screening sensitivity was larger for our continuous tumour growth model than it was for logistic regression. For the settings considered in this paper, the percentage increase in power ranged from 34 to 56%. In our analysis of data from Swedish breast cancer patients, using our continuous growth model, when including both percentage mammographic density and dense tissue scatter in the screening sensitivity submodel, only the latter variable was significantly associated with sensitivity. When included one at a time, both markers were significantly associated (p-values of 5.7 × 10−3 and 1.0 × 10−5 for percentage mammographic density and dense tissue scatter, respectively). Conclusions Our continuous tumour growth model is useful for finding image markers of screening sensitivity and for quantifying their role, using large scale epidemiological and screening data. Clustered dense tissue is associated with low mammography screening sensitivity.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90798830","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 : 2020-01-01Epub Date: 2020-02-25DOI: 10.1515/em-2019-0032
Andrea Bellavia, Ran S Rotem, Aisha S Dickerson, Johnni Hansen, Ole Gredal, Marc G Weisskopf
Investigating the joint exposure to several risk factors is becoming a key component of epidemiologic studies. Individuals are exposed to multiple factors, often simultaneously, and evaluating patterns of exposures and high-dimension interactions may allow for a better understanding of health risks at the individual level. When jointly evaluating high-dimensional exposures, common statistical methods should be integrated with machine learning techniques that may better account for complex settings. Among these, Logic regression was developed to investigate a large number of binary exposures as they relate to a given outcome. This method may be of interest in several public health settings, yet has never been presented to an epidemiologic audience. In this paper, we review and discuss Logic regression as a potential tool for epidemiological studies, using an example of occupation history (68 binary exposures of primary occupations) and amyotrophic lateral sclerosis in a population-based Danish cohort. Logic regression identifies predictors that are Boolean combinations of the original (binary) exposures, fully operating within the regression framework of interest (e.g. linear, logistic). Combinations of exposures are graphically presented as Logic trees, and techniques for selecting the best Logic model are available and of high importance. While highlighting several advantages of the method, we also discuss specific drawbacks and practical issues that should be considered when using Logic regression in population-based studies. With this paper, we encourage researchers to explore the use of machine learning techniques when evaluating large-dimensional epidemiologic data, as well as advocate the need of further methodological work in the area.
{"title":"The use of Logic regression in epidemiologic studies to investigate multiple binary exposures: an example of occupation history and amyotrophic lateral sclerosis.","authors":"Andrea Bellavia, Ran S Rotem, Aisha S Dickerson, Johnni Hansen, Ole Gredal, Marc G Weisskopf","doi":"10.1515/em-2019-0032","DOIUrl":"https://doi.org/10.1515/em-2019-0032","url":null,"abstract":"<p><p>Investigating the joint exposure to several risk factors is becoming a key component of epidemiologic studies. Individuals are exposed to multiple factors, often simultaneously, and evaluating patterns of exposures and high-dimension interactions may allow for a better understanding of health risks at the individual level. When jointly evaluating high-dimensional exposures, common statistical methods should be integrated with machine learning techniques that may better account for complex settings. Among these, Logic regression was developed to investigate a large number of binary exposures as they relate to a given outcome. This method may be of interest in several public health settings, yet has never been presented to an epidemiologic audience. In this paper, we review and discuss Logic regression as a potential tool for epidemiological studies, using an example of occupation history (68 binary exposures of primary occupations) and amyotrophic lateral sclerosis in a population-based Danish cohort. Logic regression identifies predictors that are Boolean combinations of the original (binary) exposures, fully operating within the regression framework of interest (e.g. linear, logistic). Combinations of exposures are graphically presented as Logic trees, and techniques for selecting the best Logic model are available and of high importance. While highlighting several advantages of the method, we also discuss specific drawbacks and practical issues that should be considered when using Logic regression in population-based studies. With this paper, we encourage researchers to explore the use of machine learning techniques when evaluating large-dimensional epidemiologic data, as well as advocate the need of further methodological work in the area.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/em-2019-0032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38635080","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}
Abstract Clinic-based cohort studies enroll patients on first being admitted to the clinic, and follow them as part of usual care, with interest being in the marginal mean of the outcome process. As the required frequency of follow-up varies among patients, these studies often feature irregular visit times, with no two patients sharing a visit time. Inverse-intensity weighting has been developed to handle this, however it requires that the visit process be conditionally independent of the outcome given the observed history. When patients schedule visits in response to changes in their health (for example a disease flare), the conditional independence assumption is no longer plausible, leading to biased results. We suggest additional information that can be collected to ensure that conditional independence holds, and examine how this might be used in the analysis. This allows clinic-based cohort studies to be used to determine longitudinal outcomes without incurring bias due to irregular follow-up.
{"title":"Meeting the Assumptions of Inverse-Intensity Weighting for Longitudinal Data Subject to Irregular Follow-Up: Suggestions for the Design and Analysis of Clinic-Based Cohort Studies","authors":"E. Pullenayegum","doi":"10.1515/em-2018-0016","DOIUrl":"https://doi.org/10.1515/em-2018-0016","url":null,"abstract":"Abstract Clinic-based cohort studies enroll patients on first being admitted to the clinic, and follow them as part of usual care, with interest being in the marginal mean of the outcome process. As the required frequency of follow-up varies among patients, these studies often feature irregular visit times, with no two patients sharing a visit time. Inverse-intensity weighting has been developed to handle this, however it requires that the visit process be conditionally independent of the outcome given the observed history. When patients schedule visits in response to changes in their health (for example a disease flare), the conditional independence assumption is no longer plausible, leading to biased results. We suggest additional information that can be collected to ensure that conditional independence holds, and examine how this might be used in the analysis. This allows clinic-based cohort studies to be used to determine longitudinal outcomes without incurring bias due to irregular follow-up.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91146608","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 This study aimed to assess the sleep quality (habits and disorders) and the daytime sleepiness among medical students. Methods A cross-sectional questionnaire-based study was conducted during September 2018, through November 2018 at the Faculty of Medicine, Tanta University, Egypt. The study recruited undergraduate Egyptian and Malaysian students and applied a modified form of two questionnaires, namely the Sleep Habits and Life Style and the Epworth Sleepiness Scale (ESS)”. Statistical analysis was done using SPSS. The results were expressed as frequency, percentage, and mean ± standard deviation (SD). Chi-square test was used to explore associations between categorical variables. An independent sample t-test was used to detect the mean differences between groups. Ordinal regression analyses were done on the ESS findings in relation to demographics and sleep habits. p-values<0.05 were accepted as statistically significant. Results The study included 899 medical students. Most of the participants were Egyptians (67%), rural residents (57.4%), and in the preclinical stage (79.5%). Males represented 66.0% of the study participants and participants average age (SD) was 21.98 (1.13) years. The average durations (SD) of night sleep were 7.3 (1.6) hours in work days and 8.7 (2.1) hours during the weekends. Both were significantly longer among young (<21 years-old) and preclinical students (p<0.05). Students had on average (SD) 1.33 (0.29) hours duration of napping, but 60% of the participants never or rarely scheduled for napping. Larger proportion of male and Malaysian students sometimes scheduled for napping more significantly than their peers (p<0.05). Only 16.24% of students reported that the cause of daytime napping was no enough sleep at night. The students reported sleep disorders of insomnia in the form of waking up too early, trouble falling asleep, or waking up at night with failure to re-sleep (31, 30, and 26%, respectively). Snoring (22.2%) and restless legs (22.0%) were also reported by the students. High chances of dozing off was reported by 22.02% of the participants, of which 10% used sleeping pills, 41.4% suffered psychological affection, and 34.8% reported life pattern affection. We found an increased chance of daytime sleepiness among males (0.430 times) and Egyptian (2.018 times) students. There was a decreased chance of daytime sleepiness in students from rural areas and those below 21-years-old (0.262 and 0.343 times, respectively). Absence of chronic diseases suffering was significantly associated with 5.573 more chance of daytime sleepiness or dozing off. In addition, enough and average sleep at night significantly decreased the chance of daytime sleepiness by 6.292 and 6.578, respectively, whereas daytime consumption of caffeinated beverages significantly decreased the chance of daytime sleepiness by 0.341. Conclusion There was unbalanced sleep duration in work days and weekends as well as lack of scheduling
{"title":"Sleep habits and their association with daytime sleepiness among medical students of Tanta University, Egypt","authors":"Salwa A. Atlam, H. Elsabagh","doi":"10.1515/em-2019-0034","DOIUrl":"https://doi.org/10.1515/em-2019-0034","url":null,"abstract":"Abstract Objectives This study aimed to assess the sleep quality (habits and disorders) and the daytime sleepiness among medical students. Methods A cross-sectional questionnaire-based study was conducted during September 2018, through November 2018 at the Faculty of Medicine, Tanta University, Egypt. The study recruited undergraduate Egyptian and Malaysian students and applied a modified form of two questionnaires, namely the Sleep Habits and Life Style and the Epworth Sleepiness Scale (ESS)”. Statistical analysis was done using SPSS. The results were expressed as frequency, percentage, and mean ± standard deviation (SD). Chi-square test was used to explore associations between categorical variables. An independent sample t-test was used to detect the mean differences between groups. Ordinal regression analyses were done on the ESS findings in relation to demographics and sleep habits. p-values<0.05 were accepted as statistically significant. Results The study included 899 medical students. Most of the participants were Egyptians (67%), rural residents (57.4%), and in the preclinical stage (79.5%). Males represented 66.0% of the study participants and participants average age (SD) was 21.98 (1.13) years. The average durations (SD) of night sleep were 7.3 (1.6) hours in work days and 8.7 (2.1) hours during the weekends. Both were significantly longer among young (<21 years-old) and preclinical students (p<0.05). Students had on average (SD) 1.33 (0.29) hours duration of napping, but 60% of the participants never or rarely scheduled for napping. Larger proportion of male and Malaysian students sometimes scheduled for napping more significantly than their peers (p<0.05). Only 16.24% of students reported that the cause of daytime napping was no enough sleep at night. The students reported sleep disorders of insomnia in the form of waking up too early, trouble falling asleep, or waking up at night with failure to re-sleep (31, 30, and 26%, respectively). Snoring (22.2%) and restless legs (22.0%) were also reported by the students. High chances of dozing off was reported by 22.02% of the participants, of which 10% used sleeping pills, 41.4% suffered psychological affection, and 34.8% reported life pattern affection. We found an increased chance of daytime sleepiness among males (0.430 times) and Egyptian (2.018 times) students. There was a decreased chance of daytime sleepiness in students from rural areas and those below 21-years-old (0.262 and 0.343 times, respectively). Absence of chronic diseases suffering was significantly associated with 5.573 more chance of daytime sleepiness or dozing off. In addition, enough and average sleep at night significantly decreased the chance of daytime sleepiness by 6.292 and 6.578, respectively, whereas daytime consumption of caffeinated beverages significantly decreased the chance of daytime sleepiness by 0.341. Conclusion There was unbalanced sleep duration in work days and weekends as well as lack of scheduling","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79628016","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 The methods to estimate the population attributable risk (PAR) of a single risk factor or the combined PAR of multiple risk factors have been extensively studied and well developed. Ideally, the estimation of combined PAR of multiple risk factors should be based on large cohort studies, which account for both the joint distributions of risk exposures and for their interactions. However, because such individual-level data are often lacking, many studies estimate the combined PAR using a comparative risk assessment framework. It involves estimating PAR of each risk factor based on its prevalence and relative risk, and then combining the individual PARs using an approach that relies on two key assumptions: that the distributions of exposures to the risk factors are independent and that the relative risks are multiplicative. While such assumptions rarely hold true in practice, no studies have investigated the magnitude of bias incurred if the assumptions are violated. Methods Using simulation-based models, we compared the combined PARs obtained with this approach to the more accurate estimates of PARs that are available when the joint distributions of exposures and risks can be established. Results We show that the assumptions of exposure independence and risk multiplicativity are sufficient but not necessary for the combined PAR to be unbiased. In the simplest situation of two risk factors, the bias of this approach is a function of the strength of association and the magnitude of risk interaction, for any values of exposure prevalence and their associated risks. In some cases, the combined PAR can be strongly under- or over-estimated, even if the two assumptions are only slightly violated. Conclusions We encourage researchers to quantify likely biases in their use of the M–S method, and here, we provided level plots and R code to assist.
{"title":"A comparison of approaches for estimating combined population attributable risks (PARs) for multiple risk factors","authors":"Y. Ruan, S. Walter, C. Friedenreich, D. Brenner","doi":"10.1515/em-2019-0021","DOIUrl":"https://doi.org/10.1515/em-2019-0021","url":null,"abstract":"Abstract Objectives The methods to estimate the population attributable risk (PAR) of a single risk factor or the combined PAR of multiple risk factors have been extensively studied and well developed. Ideally, the estimation of combined PAR of multiple risk factors should be based on large cohort studies, which account for both the joint distributions of risk exposures and for their interactions. However, because such individual-level data are often lacking, many studies estimate the combined PAR using a comparative risk assessment framework. It involves estimating PAR of each risk factor based on its prevalence and relative risk, and then combining the individual PARs using an approach that relies on two key assumptions: that the distributions of exposures to the risk factors are independent and that the relative risks are multiplicative. While such assumptions rarely hold true in practice, no studies have investigated the magnitude of bias incurred if the assumptions are violated. Methods Using simulation-based models, we compared the combined PARs obtained with this approach to the more accurate estimates of PARs that are available when the joint distributions of exposures and risks can be established. Results We show that the assumptions of exposure independence and risk multiplicativity are sufficient but not necessary for the combined PAR to be unbiased. In the simplest situation of two risk factors, the bias of this approach is a function of the strength of association and the magnitude of risk interaction, for any values of exposure prevalence and their associated risks. In some cases, the combined PAR can be strongly under- or over-estimated, even if the two assumptions are only slightly violated. Conclusions We encourage researchers to quantify likely biases in their use of the M–S method, and here, we provided level plots and R code to assist.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84028539","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}
Anna S. Frank, D. Matteson, H. Solvang, A. Lupattelli, H. Nordeng
Abstract This manuscript extends the definition of the Absolute Standardized Mean Difference (ASMD) for binary exposure (M = 2) to cases for M > 2 on multiple imputed data sets. The Maximal Maximized Standardized Difference (MMSD) and the Maximal Averaged Standardized Difference (MASD) were proposed. For different percentages, missing data were introduced in covariates in the simulated data based on the missing at random (MAR) assumption. We then investigate the performance of these two metric definitions using simulated data of full and imputed data sets. The performance of the MASD and the MMSD were validated by relating the balance metrics to estimation bias. The results show that there is an association between the balance metrics and bias. The proposed balance diagnostics seem therefore appropriate to assess balance for the generalized propensity score (GPS) under multiple imputation.
本文将二元暴露(M = 2)的绝对标准化平均差(ASMD)的定义扩展到多个输入数据集上M > 2的情况。提出了最大最大标准化差(MMSD)和最大平均标准化差(MASD)。基于随机缺失(missing at random, MAR)假设，在模拟数据的协变量中引入不同百分比的缺失数据。然后，我们使用完整和输入数据集的模拟数据来研究这两种度量定义的性能。通过将平衡度量与估计偏差相关联，验证了MASD和MMSD的性能。结果表明，在平衡指标和偏差之间存在关联。因此，所提出的平衡诊断似乎适合于评估多重归算下广义倾向评分(GPS)的平衡。
{"title":"Extending balance assessment for the generalized propensity score under multiple imputation","authors":"Anna S. Frank, D. Matteson, H. Solvang, A. Lupattelli, H. Nordeng","doi":"10.1515/em-2019-0003","DOIUrl":"https://doi.org/10.1515/em-2019-0003","url":null,"abstract":"Abstract This manuscript extends the definition of the Absolute Standardized Mean Difference (ASMD) for binary exposure (M = 2) to cases for M > 2 on multiple imputed data sets. The Maximal Maximized Standardized Difference (MMSD) and the Maximal Averaged Standardized Difference (MASD) were proposed. For different percentages, missing data were introduced in covariates in the simulated data based on the missing at random (MAR) assumption. We then investigate the performance of these two metric definitions using simulated data of full and imputed data sets. The performance of the MASD and the MMSD were validated by relating the balance metrics to estimation bias. The results show that there is an association between the balance metrics and bias. The proposed balance diagnostics seem therefore appropriate to assess balance for the generalized propensity score (GPS) under multiple imputation.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80279658","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. B. Rose, J. Roy, R. Castillo-Neyra, M. Ross, C. Condori-Pino, J. Peterson, César Náquira-Velarde, M. Levy
Abstract Objectives Containing domestic vector infestation requires the ability to swiftly locate and treat infested homes. In urban settings where vectors are heterogeneously distributed throughout a dense housing matrix, the task of locating infestations can be challenging. Here, we present a novel stochastic compartmental model developed to help locate infested homes in urban areas. We designed the model using infestation data for the Chagas disease vector species Triatoma infestans in Arequipa, Peru. Methods Our approach incorporates disease vector counts at each observed house, and the vector’s complex spatial dispersal dynamics. We used a Bayesian method to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigated the properties of the model through simulation studies, followed by field testing in Arequipa. Results Simulation studies showed the model to be accurate in its estimates of two parameters of interest: the growth rate of a domestic triatomine bug colony and the probability of a triatomine bug successfully invading a new home after dispersing from an infested home. When testing the model in the field, data collection using model estimates was hindered by low household participation rates, which severely limited the algorithm and in turn, the model’s predictive power. Conclusions While future optimization efforts must improve the model’s capabilities when household participation is low, our approach is nonetheless an important step toward integrating data with predictive modeling to carry out evidence-based vector surveillance in cities.
{"title":"A real-time search strategy for finding urban disease vector infestations","authors":"E. B. Rose, J. Roy, R. Castillo-Neyra, M. Ross, C. Condori-Pino, J. Peterson, César Náquira-Velarde, M. Levy","doi":"10.1515/em-2020-0001","DOIUrl":"https://doi.org/10.1515/em-2020-0001","url":null,"abstract":"Abstract Objectives Containing domestic vector infestation requires the ability to swiftly locate and treat infested homes. In urban settings where vectors are heterogeneously distributed throughout a dense housing matrix, the task of locating infestations can be challenging. Here, we present a novel stochastic compartmental model developed to help locate infested homes in urban areas. We designed the model using infestation data for the Chagas disease vector species Triatoma infestans in Arequipa, Peru. Methods Our approach incorporates disease vector counts at each observed house, and the vector’s complex spatial dispersal dynamics. We used a Bayesian method to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigated the properties of the model through simulation studies, followed by field testing in Arequipa. Results Simulation studies showed the model to be accurate in its estimates of two parameters of interest: the growth rate of a domestic triatomine bug colony and the probability of a triatomine bug successfully invading a new home after dispersing from an infested home. When testing the model in the field, data collection using model estimates was hindered by low household participation rates, which severely limited the algorithm and in turn, the model’s predictive power. Conclusions While future optimization efforts must improve the model’s capabilities when household participation is low, our approach is nonetheless an important step toward integrating data with predictive modeling to carry out evidence-based vector surveillance in cities.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89387382","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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