Editorial: Robust Sensitivities
Am J Epidemiol. 2021 Mar 29:kwab071. doi: 10.1093/aje/kwab071. Online ahead of print.NO ABSTRACTPMID:33778853 | DOI:10.1093/aje/kwab071 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Catherine R Lesko Stephen R Cole Enrique F Schisterman Source Type: research

Trends in "Deaths of Despair" among Working Aged White and Black Americans, 1990-2017
Am J Epidemiol. 2021 Mar 29:kwab088. doi: 10.1093/aje/kwab088. Online ahead of print.ABSTRACTLife expectancy for U.S. white men and women declined between 2013 and 2017. Initial explanations for the decline focused on increases in "deaths of despair" (i.e., deaths from suicide, drug use, and alcohol use), which have been interpreted as a cohort-based phenomenon afflicting middle-aged white Americans. There has been less attention on black mortality trends from these same causes, and whether the trends are similar or different by cohort and period. We complement existing research and contend that recent mortality ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Andrea M Tilstra Daniel H Simon Ryan K Masters Source Type: research

Invited Commentary: Toward Better Bias Analysis
Am J Epidemiol. 2021 Mar 29:kwab068. doi: 10.1093/aje/kwab068. Online ahead of print.ABSTRACTThe article by Lash et al (Am J. Epidemiol.) shows how some previously published bias analyses could have been better. Via investigation of one of their examples, we add some thoughts about routes to better bias analysis, particularly via exploration of a joint distribution of bias parameters and target parameters.PMID:33778860 | DOI:10.1093/aje/kwab068 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Paul Gustafson Source Type: research

Dealing with the Inevitable Deficiencies of Bias Analysis - and All Analyses
Am J Epidemiol. 2021 Mar 29:kwab069. doi: 10.1093/aje/kwab069. Online ahead of print.ABSTRACTLash et al. (Am. J. Epidemiol. xxx) present detailed critiques of 3 bias analyses which they identify as "suboptimal". This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies - with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full account...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Sander Greenland Source Type: research

Gestational Exposure to Toxicants and Autistic Behaviors using Bayesian Quantile Regression
Am J Epidemiol. 2021 Mar 29:kwab065. doi: 10.1093/aje/kwab065. Online ahead of print.ABSTRACTAutism Spectrum Disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1-2% of children. While prenatal exposure to toxicants has been associated with autistic behaviors, most studies have focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pa...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Joshua D Alampi Bruce P Lanphear Joseph M Braun Aimen Chen Tim K Takaro Gina Muckle Tye E Arbuckle Lawrence C McCandless Source Type: research

Data-Driven Model Building for Life Course Epidemiology
Am J Epidemiol. 2021 Mar 29:kwab087. doi: 10.1093/aje/kwab087. Online ahead of print.ABSTRACTLife course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, as they rely on a priori model specification. This limits the scope of causal inquiries that can be made, since these methods are mostly suited to examine well-known hypotheses that do not question our established view of health, which may lead to confirmation bias. We propose an exploratory alternative. Instead of specifyinga life course model prior t...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Anne H Petersen Merete Osler Claus T Ekstr øm Source Type: research

Maternal Urinary Organophosphate Esters and Alterations in Maternal and Neonatal Thyroid Hormones
Am J Epidemiol. 2021 Mar 29:kwab086. doi: 10.1093/aje/kwab086. Online ahead of print.ABSTRACTProduction of organophosphate esters (OPEs), which represent a major flame retardant class present in consumer goods, has risen over the past two decades. Experimental studies suggest that OPEs may be associated with thyroid hormone disruption, but few human studies have examined this association. We quantified OPE metabolites in the urine of 298 pregnant women in the Health Outcomes and Measures of the Environment Study from Cincinnati, Ohio (enrolled 2003-2006) at three time points (16 and 26 weeks' gestation, delivery), and thyr...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Zana Percy Ann M Vuong Yingying Xu Changchun Xie Maria Ospina Antonia M Calafat Andy Hoofnagle Bruce P Lanphear Joseph M Braun Kim M Cecil Kim N Dietrich Kimberly Yolton Aimin Chen Source Type: research

Lash et al. Respond to "Dealing with the Inevitable Deficiencies of Bias Analysis-and All Analyses" and "Toward Better Bias Analysis"
Am J Epidemiol. 2021 Mar 29:kwab070. doi: 10.1093/aje/kwab070. Online ahead of print.NO ABSTRACTPMID:33778843 | DOI:10.1093/aje/kwab070 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Bias Analysis Gone Bad
Am J Epidemiol. 2021 Mar 29:kwab072. doi: 10.1093/aje/kwab072. Online ahead of print.ABSTRACTQuantitative Bias Analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional, but may occasionally include intentional efforts to mislead. We identified three ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Editorial: Robust Sensitivities
Am J Epidemiol. 2021 Mar 29:kwab071. doi: 10.1093/aje/kwab071. Online ahead of print.NO ABSTRACTPMID:33778853 | DOI:10.1093/aje/kwab071 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Catherine R Lesko Stephen R Cole Enrique F Schisterman Source Type: research

Trends in "Deaths of Despair" among Working Aged White and Black Americans, 1990-2017
Am J Epidemiol. 2021 Mar 29:kwab088. doi: 10.1093/aje/kwab088. Online ahead of print.ABSTRACTLife expectancy for U.S. white men and women declined between 2013 and 2017. Initial explanations for the decline focused on increases in "deaths of despair" (i.e., deaths from suicide, drug use, and alcohol use), which have been interpreted as a cohort-based phenomenon afflicting middle-aged white Americans. There has been less attention on black mortality trends from these same causes, and whether the trends are similar or different by cohort and period. We complement existing research and contend that recent mortality ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Andrea M Tilstra Daniel H Simon Ryan K Masters Source Type: research

Invited Commentary: Toward Better Bias Analysis
Am J Epidemiol. 2021 Mar 29:kwab068. doi: 10.1093/aje/kwab068. Online ahead of print.ABSTRACTThe article by Lash et al (Am J. Epidemiol.) shows how some previously published bias analyses could have been better. Via investigation of one of their examples, we add some thoughts about routes to better bias analysis, particularly via exploration of a joint distribution of bias parameters and target parameters.PMID:33778860 | DOI:10.1093/aje/kwab068 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Paul Gustafson Source Type: research

Dealing with the Inevitable Deficiencies of Bias Analysis - and All Analyses
Am J Epidemiol. 2021 Mar 29:kwab069. doi: 10.1093/aje/kwab069. Online ahead of print.ABSTRACTLash et al. (Am. J. Epidemiol. xxx) present detailed critiques of 3 bias analyses which they identify as "suboptimal". This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies - with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full account...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Sander Greenland Source Type: research

Gestational Exposure to Toxicants and Autistic Behaviors using Bayesian Quantile Regression
Am J Epidemiol. 2021 Mar 29:kwab065. doi: 10.1093/aje/kwab065. Online ahead of print.ABSTRACTAutism Spectrum Disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1-2% of children. While prenatal exposure to toxicants has been associated with autistic behaviors, most studies have focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pa...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Joshua D Alampi Bruce P Lanphear Joseph M Braun Aimen Chen Tim K Takaro Gina Muckle Tye E Arbuckle Lawrence C McCandless Source Type: research

Data-Driven Model Building for Life Course Epidemiology
Am J Epidemiol. 2021 Mar 29:kwab087. doi: 10.1093/aje/kwab087. Online ahead of print.ABSTRACTLife course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, as they rely on a priori model specification. This limits the scope of causal inquiries that can be made, since these methods are mostly suited to examine well-known hypotheses that do not question our established view of health, which may lead to confirmation bias. We propose an exploratory alternative. Instead of specifyinga life course model prior t...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Anne H Petersen Merete Osler Claus T Ekstr øm Source Type: research

Maternal Urinary Organophosphate Esters and Alterations in Maternal and Neonatal Thyroid Hormones
Am J Epidemiol. 2021 Mar 29:kwab086. doi: 10.1093/aje/kwab086. Online ahead of print.ABSTRACTProduction of organophosphate esters (OPEs), which represent a major flame retardant class present in consumer goods, has risen over the past two decades. Experimental studies suggest that OPEs may be associated with thyroid hormone disruption, but few human studies have examined this association. We quantified OPE metabolites in the urine of 298 pregnant women in the Health Outcomes and Measures of the Environment Study from Cincinnati, Ohio (enrolled 2003-2006) at three time points (16 and 26 weeks' gestation, delivery), and thyr...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Zana Percy Ann M Vuong Yingying Xu Changchun Xie Maria Ospina Antonia M Calafat Andy Hoofnagle Bruce P Lanphear Joseph M Braun Kim M Cecil Kim N Dietrich Kimberly Yolton Aimin Chen Source Type: research

Lash et al. Respond to "Dealing with the Inevitable Deficiencies of Bias Analysis-and All Analyses" and "Toward Better Bias Analysis"
Am J Epidemiol. 2021 Mar 29:kwab070. doi: 10.1093/aje/kwab070. Online ahead of print.NO ABSTRACTPMID:33778843 | DOI:10.1093/aje/kwab070 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Bias Analysis Gone Bad
Am J Epidemiol. 2021 Mar 29:kwab072. doi: 10.1093/aje/kwab072. Online ahead of print.ABSTRACTQuantitative Bias Analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional, but may occasionally include intentional efforts to mislead. We identified three ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Editorial: Robust Sensitivities
Am J Epidemiol. 2021 Mar 29:kwab071. doi: 10.1093/aje/kwab071. Online ahead of print.NO ABSTRACTPMID:33778853 | DOI:10.1093/aje/kwab071 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Catherine R Lesko Stephen R Cole Enrique F Schisterman Source Type: research

Trends in "Deaths of Despair" among Working Aged White and Black Americans, 1990-2017
Am J Epidemiol. 2021 Mar 29:kwab088. doi: 10.1093/aje/kwab088. Online ahead of print.ABSTRACTLife expectancy for U.S. white men and women declined between 2013 and 2017. Initial explanations for the decline focused on increases in "deaths of despair" (i.e., deaths from suicide, drug use, and alcohol use), which have been interpreted as a cohort-based phenomenon afflicting middle-aged white Americans. There has been less attention on black mortality trends from these same causes, and whether the trends are similar or different by cohort and period. We complement existing research and contend that recent mortality ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Andrea M Tilstra Daniel H Simon Ryan K Masters Source Type: research

Invited Commentary: Toward Better Bias Analysis
Am J Epidemiol. 2021 Mar 29:kwab068. doi: 10.1093/aje/kwab068. Online ahead of print.ABSTRACTThe article by Lash et al (Am J. Epidemiol.) shows how some previously published bias analyses could have been better. Via investigation of one of their examples, we add some thoughts about routes to better bias analysis, particularly via exploration of a joint distribution of bias parameters and target parameters.PMID:33778860 | DOI:10.1093/aje/kwab068 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Paul Gustafson Source Type: research

Dealing with the Inevitable Deficiencies of Bias Analysis - and All Analyses
Am J Epidemiol. 2021 Mar 29:kwab069. doi: 10.1093/aje/kwab069. Online ahead of print.ABSTRACTLash et al. (Am. J. Epidemiol. xxx) present detailed critiques of 3 bias analyses which they identify as "suboptimal". This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies - with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full account...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Sander Greenland Source Type: research

Gestational Exposure to Toxicants and Autistic Behaviors using Bayesian Quantile Regression
Am J Epidemiol. 2021 Mar 29:kwab065. doi: 10.1093/aje/kwab065. Online ahead of print.ABSTRACTAutism Spectrum Disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1-2% of children. While prenatal exposure to toxicants has been associated with autistic behaviors, most studies have focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pa...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Joshua D Alampi Bruce P Lanphear Joseph M Braun Aimen Chen Tim K Takaro Gina Muckle Tye E Arbuckle Lawrence C McCandless Source Type: research

Data-Driven Model Building for Life Course Epidemiology
Am J Epidemiol. 2021 Mar 29:kwab087. doi: 10.1093/aje/kwab087. Online ahead of print.ABSTRACTLife course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, as they rely on a priori model specification. This limits the scope of causal inquiries that can be made, since these methods are mostly suited to examine well-known hypotheses that do not question our established view of health, which may lead to confirmation bias. We propose an exploratory alternative. Instead of specifyinga life course model prior t...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Anne H Petersen Merete Osler Claus T Ekstr øm Source Type: research

Maternal Urinary Organophosphate Esters and Alterations in Maternal and Neonatal Thyroid Hormones
Am J Epidemiol. 2021 Mar 29:kwab086. doi: 10.1093/aje/kwab086. Online ahead of print.ABSTRACTProduction of organophosphate esters (OPEs), which represent a major flame retardant class present in consumer goods, has risen over the past two decades. Experimental studies suggest that OPEs may be associated with thyroid hormone disruption, but few human studies have examined this association. We quantified OPE metabolites in the urine of 298 pregnant women in the Health Outcomes and Measures of the Environment Study from Cincinnati, Ohio (enrolled 2003-2006) at three time points (16 and 26 weeks' gestation, delivery), and thyr...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Zana Percy Ann M Vuong Yingying Xu Changchun Xie Maria Ospina Antonia M Calafat Andy Hoofnagle Bruce P Lanphear Joseph M Braun Kim M Cecil Kim N Dietrich Kimberly Yolton Aimin Chen Source Type: research

Lash et al. Respond to "Dealing with the Inevitable Deficiencies of Bias Analysis-and All Analyses" and "Toward Better Bias Analysis"
Am J Epidemiol. 2021 Mar 29:kwab070. doi: 10.1093/aje/kwab070. Online ahead of print.NO ABSTRACTPMID:33778843 | DOI:10.1093/aje/kwab070 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Bias Analysis Gone Bad
Am J Epidemiol. 2021 Mar 29:kwab072. doi: 10.1093/aje/kwab072. Online ahead of print.ABSTRACTQuantitative Bias Analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional, but may occasionally include intentional efforts to mislead. We identified three ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Editorial: Robust Sensitivities
Am J Epidemiol. 2021 Mar 29:kwab071. doi: 10.1093/aje/kwab071. Online ahead of print.NO ABSTRACTPMID:33778853 | DOI:10.1093/aje/kwab071 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Catherine R Lesko Stephen R Cole Enrique F Schisterman Source Type: research

Trends in "Deaths of Despair" among Working Aged White and Black Americans, 1990-2017
Am J Epidemiol. 2021 Mar 29:kwab088. doi: 10.1093/aje/kwab088. Online ahead of print.ABSTRACTLife expectancy for U.S. white men and women declined between 2013 and 2017. Initial explanations for the decline focused on increases in "deaths of despair" (i.e., deaths from suicide, drug use, and alcohol use), which have been interpreted as a cohort-based phenomenon afflicting middle-aged white Americans. There has been less attention on black mortality trends from these same causes, and whether the trends are similar or different by cohort and period. We complement existing research and contend that recent mortality ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Andrea M Tilstra Daniel H Simon Ryan K Masters Source Type: research

Invited Commentary: Toward Better Bias Analysis
Am J Epidemiol. 2021 Mar 29:kwab068. doi: 10.1093/aje/kwab068. Online ahead of print.ABSTRACTThe article by Lash et al (Am J. Epidemiol.) shows how some previously published bias analyses could have been better. Via investigation of one of their examples, we add some thoughts about routes to better bias analysis, particularly via exploration of a joint distribution of bias parameters and target parameters.PMID:33778860 | DOI:10.1093/aje/kwab068 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Paul Gustafson Source Type: research

Dealing with the Inevitable Deficiencies of Bias Analysis - and All Analyses
Am J Epidemiol. 2021 Mar 29:kwab069. doi: 10.1093/aje/kwab069. Online ahead of print.ABSTRACTLash et al. (Am. J. Epidemiol. xxx) present detailed critiques of 3 bias analyses which they identify as "suboptimal". This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies - with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full account...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Sander Greenland Source Type: research

Gestational Exposure to Toxicants and Autistic Behaviors using Bayesian Quantile Regression
Am J Epidemiol. 2021 Mar 29:kwab065. doi: 10.1093/aje/kwab065. Online ahead of print.ABSTRACTAutism Spectrum Disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1-2% of children. While prenatal exposure to toxicants has been associated with autistic behaviors, most studies have focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pa...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Joshua D Alampi Bruce P Lanphear Joseph M Braun Aimen Chen Tim K Takaro Gina Muckle Tye E Arbuckle Lawrence C McCandless Source Type: research

Data-Driven Model Building for Life Course Epidemiology
Am J Epidemiol. 2021 Mar 29:kwab087. doi: 10.1093/aje/kwab087. Online ahead of print.ABSTRACTLife course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, as they rely on a priori model specification. This limits the scope of causal inquiries that can be made, since these methods are mostly suited to examine well-known hypotheses that do not question our established view of health, which may lead to confirmation bias. We propose an exploratory alternative. Instead of specifyinga life course model prior t...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Anne H Petersen Merete Osler Claus T Ekstr øm Source Type: research

Maternal Urinary Organophosphate Esters and Alterations in Maternal and Neonatal Thyroid Hormones
Am J Epidemiol. 2021 Mar 29:kwab086. doi: 10.1093/aje/kwab086. Online ahead of print.ABSTRACTProduction of organophosphate esters (OPEs), which represent a major flame retardant class present in consumer goods, has risen over the past two decades. Experimental studies suggest that OPEs may be associated with thyroid hormone disruption, but few human studies have examined this association. We quantified OPE metabolites in the urine of 298 pregnant women in the Health Outcomes and Measures of the Environment Study from Cincinnati, Ohio (enrolled 2003-2006) at three time points (16 and 26 weeks' gestation, delivery), and thyr...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Zana Percy Ann M Vuong Yingying Xu Changchun Xie Maria Ospina Antonia M Calafat Andy Hoofnagle Bruce P Lanphear Joseph M Braun Kim M Cecil Kim N Dietrich Kimberly Yolton Aimin Chen Source Type: research

Lash et al. Respond to "Dealing with the Inevitable Deficiencies of Bias Analysis-and All Analyses" and "Toward Better Bias Analysis"
Am J Epidemiol. 2021 Mar 29:kwab070. doi: 10.1093/aje/kwab070. Online ahead of print.NO ABSTRACTPMID:33778843 | DOI:10.1093/aje/kwab070 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Bias Analysis Gone Bad
Am J Epidemiol. 2021 Mar 29:kwab072. doi: 10.1093/aje/kwab072. Online ahead of print.ABSTRACTQuantitative Bias Analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional, but may occasionally include intentional efforts to mislead. We identified three ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Timothy L Lash Thomas P Ahern Lindsay J Collin Matthew P Fox Richard F MacLehose Source Type: research

Editorial: Robust Sensitivities
Am J Epidemiol. 2021 Mar 29:kwab071. doi: 10.1093/aje/kwab071. Online ahead of print.NO ABSTRACTPMID:33778853 | DOI:10.1093/aje/kwab071 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Catherine R Lesko Stephen R Cole Enrique F Schisterman Source Type: research

Trends in "Deaths of Despair" among Working Aged White and Black Americans, 1990-2017
Am J Epidemiol. 2021 Mar 29:kwab088. doi: 10.1093/aje/kwab088. Online ahead of print.ABSTRACTLife expectancy for U.S. white men and women declined between 2013 and 2017. Initial explanations for the decline focused on increases in "deaths of despair" (i.e., deaths from suicide, drug use, and alcohol use), which have been interpreted as a cohort-based phenomenon afflicting middle-aged white Americans. There has been less attention on black mortality trends from these same causes, and whether the trends are similar or different by cohort and period. We complement existing research and contend that recent mortality ...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Andrea M Tilstra Daniel H Simon Ryan K Masters Source Type: research

Invited Commentary: Toward Better Bias Analysis
Am J Epidemiol. 2021 Mar 29:kwab068. doi: 10.1093/aje/kwab068. Online ahead of print.ABSTRACTThe article by Lash et al (Am J. Epidemiol.) shows how some previously published bias analyses could have been better. Via investigation of one of their examples, we add some thoughts about routes to better bias analysis, particularly via exploration of a joint distribution of bias parameters and target parameters.PMID:33778860 | DOI:10.1093/aje/kwab068 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Paul Gustafson Source Type: research

Dealing with the Inevitable Deficiencies of Bias Analysis - and All Analyses
Am J Epidemiol. 2021 Mar 29:kwab069. doi: 10.1093/aje/kwab069. Online ahead of print.ABSTRACTLash et al. (Am. J. Epidemiol. xxx) present detailed critiques of 3 bias analyses which they identify as "suboptimal". This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies - with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full account...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Sander Greenland Source Type: research

Gestational Exposure to Toxicants and Autistic Behaviors using Bayesian Quantile Regression
Am J Epidemiol. 2021 Mar 29:kwab065. doi: 10.1093/aje/kwab065. Online ahead of print.ABSTRACTAutism Spectrum Disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1-2% of children. While prenatal exposure to toxicants has been associated with autistic behaviors, most studies have focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pa...
Source: Am J Epidemiol - March 29, 2021 Category: Epidemiology Authors: Joshua D Alampi Bruce P Lanphear Joseph M Braun Aimen Chen Tim K Takaro Gina Muckle Tye E Arbuckle Lawrence C McCandless Source Type: research

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources
Am J Epidemiol. 2021 Mar 23:kwab077. doi: 10.1093/aje/kwab077. Online ahead of print.ABSTRACTSevere maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Bi...
Source: Am J Epidemiol - March 23, 2021 Category: Epidemiology Authors: Jonathan M Snowden Audrey Lyndon Peiyi Kan Alison El Ayadi Elliott Main Suzan L Carmichael Source Type: research

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources
Am J Epidemiol. 2021 Mar 23:kwab077. doi: 10.1093/aje/kwab077. Online ahead of print.ABSTRACTSevere maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Bi...
Source: Am J Epidemiol - March 23, 2021 Category: Epidemiology Authors: Jonathan M Snowden Audrey Lyndon Peiyi Kan Alison El Ayadi Elliott Main Suzan L Carmichael Source Type: research

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources
Am J Epidemiol. 2021 Mar 23:kwab077. doi: 10.1093/aje/kwab077. Online ahead of print.ABSTRACTSevere maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Bi...
Source: Am J Epidemiol - March 23, 2021 Category: Epidemiology Authors: Jonathan M Snowden Audrey Lyndon Peiyi Kan Alison El Ayadi Elliott Main Suzan L Carmichael Source Type: research

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources
Am J Epidemiol. 2021 Mar 23:kwab077. doi: 10.1093/aje/kwab077. Online ahead of print.ABSTRACTSevere maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Bi...
Source: Am J Epidemiol - March 23, 2021 Category: Epidemiology Authors: Jonathan M Snowden Audrey Lyndon Peiyi Kan Alison El Ayadi Elliott Main Suzan L Carmichael Source Type: research

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources
Am J Epidemiol. 2021 Mar 23:kwab077. doi: 10.1093/aje/kwab077. Online ahead of print.ABSTRACTSevere maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Bi...
Source: Am J Epidemiol - March 23, 2021 Category: Epidemiology Authors: Jonathan M Snowden Audrey Lyndon Peiyi Kan Alison El Ayadi Elliott Main Suzan L Carmichael Source Type: research

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources
Am J Epidemiol. 2021 Mar 23:kwab077. doi: 10.1093/aje/kwab077. Online ahead of print.ABSTRACTSevere maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Bi...
Source: Am J Epidemiol - March 23, 2021 Category: Epidemiology Authors: Jonathan M Snowden Audrey Lyndon Peiyi Kan Alison El Ayadi Elliott Main Suzan L Carmichael Source Type: research

13 Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!)
Am J Epidemiol. 2021 Mar 6:kwab047. doi: 10.1093/aje/kwab047. Online ahead of print.ABSTRACTMachine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of 'black box' models. We conclude with sample code that may lower the barrier ...
Source: Am J Epidemiol - March 22, 2021 Category: Epidemiology Authors: Stephen J Mooney Alexander P Keil Daniel J Westreich Source Type: research

Data Sources That Enumerate People Experiencing Homelessness in the United States: Opportunities and Challenges for Epidemiological Research
We describe the appropriate uses and limitations of each data source in the context of infectious disease epidemiology. These data sources provide an opportunity to expand current research and develop actionable analyses.PMID:33751025 | DOI:10.1093/aje/kwab051 (Source: Am J Epidemiol)
Source: Am J Epidemiol - March 22, 2021 Category: Epidemiology Authors: Emily Mosites Sapna Bamrah Morris Julie Self Jay C Butler Source Type: research

Sodium Glucose Co-Transporter-2 (SGLT2) Inhibitors and The Risk of Diabetic Ketoacidosis: An Example of Complementary Evidence for Rare Adverse Events
In this study, we conducted a systematic review and meta-analyzed data from RCTs (n=18) and observational (n=7) studies separately, to assess the consistency of the magnitude of association between SGLT-2 inhibitors and DKA. We also illustrate the strengths and weakness of the two designs. Results from RCTs and observational studies consistently show almost a doubling in the risk of DKA in patients using an SGLT-2 inhibitor compared to placebo or active comparator. Using a random-effects model, the pooled relative risk [RR], (95% confidence interval [CI]) was 2.08, (95%CI 1.28, 3.40) from placebo-controlled RCTs and was 0....
Source: Am J Epidemiol - March 22, 2021 Category: Epidemiology Authors: Wajd Alkabbani Ryan Pelletier John-Michael Gamble Source Type: research