Pub Date : 2024-09-01Epub Date: 2024-07-05DOI: 10.1097/EDE.0000000000001759
Kerollos Nashat Wanis, Aaron L Sarvet
{"title":"Placebo Adherence as a Negative Control Exposure.","authors":"Kerollos Nashat Wanis, Aaron L Sarvet","doi":"10.1097/EDE.0000000000001759","DOIUrl":"10.1097/EDE.0000000000001759","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"654-659"},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141537703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01Epub Date: 2024-08-06DOI: 10.1097/EDE.0000000000001753
Lindsay J Collin, Lance A Waller, Deirdre P Cronin-Fenton, Thomas P Ahern, Michael Goodman, Lauren E McCullough, Anders Kjærsgaard, Kirsten M Woolpert, Rebecca A Silliman, Peer M Christiansen, Bent Ejlertsen, Henrik Toft Sørensen, Timothy L Lash
Purpose: Breast cancer has an average 10-year relative survival reaching 84%. This favorable survival is due, in part, to the introduction of biomarker-guided therapies. We estimated the population-level effect of the introduction of two adjuvant therapies-tamoxifen and trastuzumab-on recurrence using the trend-in-trend pharmacoepidemiologic study design.
Methods: We ascertained data on women diagnosed with nonmetastatic breast cancer who were registered in the Danish Breast Cancer Group clinical database. We used the trend-in-trend design to estimate the population-level effect of the introduction of (1) tamoxifen for postmenopausal women with estrogen receptor (ER)-positive breast cancer in 1982, (2) tamoxifen for premenopausal women diagnosed with ER-positive breast cancer in 1999, and (3) trastuzumab for women <60 years diagnosed with human epidermal growth factor receptor 2-positive breast cancer in 2007.
Results: For the population-level effect of the introduction of tamoxifen among premenopausal women diagnosed with ER-positive breast cancer in 1999, the risk of recurrence decreased by nearly one-half (OR = 0.52), consistent with evidence from clinical trials; however, the estimate was imprecise (95% confidence interval [CI] = 0.25, 1.85). We observed an imprecise association between tamoxifen use and recurrence from the time it was introduced in 1982 (OR = 1.24 95% CI = 0.46, 5.11), inconsistent with prior knowledge from clinical trials. For the introduction of trastuzumab in 2007, the estimate was also consistent with trial evidence, though imprecise (OR = 0.51; 95% CI = 0.21, 22.4).
Conclusions: We demonstrated how novel pharmacoepidemiologic analytic designs can be used to evaluate the routine clinical care and effectiveness of therapeutic advancements in a population-based setting while considering some limitations of the approach.
目的:乳腺癌的 10 年平均相对生存率高达 84%。这种良好的生存率部分归功于生物标志物指导疗法的引入。我们采用趋势中趋势药物流行病学研究设计,估算了两种辅助疗法--他莫昔芬和曲妥珠单抗的引入对人群复发的影响:我们确定了丹麦乳腺癌小组临床数据库中登记的非转移性乳腺癌女性患者的数据。我们采用趋势中趋势的设计方法,估算了(1)1982 年对绝经后雌激素受体(ER)阳性乳腺癌妇女使用他莫昔芬、(2)1999 年对绝经前诊断为 ER 阳性乳腺癌的妇女使用他莫昔芬以及(3)对妇女使用曲妥珠单抗的人群效应:在 1999 年确诊为 ER 阳性乳腺癌的绝经前妇女中引入他莫昔芬的人群效应中,复发风险降低了近一半(OR = 0.52),这与临床试验的证据一致;然而,该估计值并不精确(95% 置信区间 [CI] = 0.25,1.85)。我们观察到,自1982年他莫昔芬问世以来,使用他莫昔芬与复发之间的关系并不精确(OR = 1.24 95% CI = 0.46, 5.11),这与之前临床试验的结果不一致。至于 2007 年引入的曲妥珠单抗,尽管不精确(OR = 0.51; 95% CI = 0.21, 22.4),但估计值也与试验证据一致:我们展示了如何利用新型药物流行病学分析设计来评估基于人群的常规临床护理和治疗进展的有效性,同时也考虑了该方法的一些局限性。
{"title":"The Population-level Effect of Adjuvant Therapies on Breast Cancer Recurrence: Application of the Trend-in-Trend Design.","authors":"Lindsay J Collin, Lance A Waller, Deirdre P Cronin-Fenton, Thomas P Ahern, Michael Goodman, Lauren E McCullough, Anders Kjærsgaard, Kirsten M Woolpert, Rebecca A Silliman, Peer M Christiansen, Bent Ejlertsen, Henrik Toft Sørensen, Timothy L Lash","doi":"10.1097/EDE.0000000000001753","DOIUrl":"10.1097/EDE.0000000000001753","url":null,"abstract":"<p><strong>Purpose: </strong>Breast cancer has an average 10-year relative survival reaching 84%. This favorable survival is due, in part, to the introduction of biomarker-guided therapies. We estimated the population-level effect of the introduction of two adjuvant therapies-tamoxifen and trastuzumab-on recurrence using the trend-in-trend pharmacoepidemiologic study design.</p><p><strong>Methods: </strong>We ascertained data on women diagnosed with nonmetastatic breast cancer who were registered in the Danish Breast Cancer Group clinical database. We used the trend-in-trend design to estimate the population-level effect of the introduction of (1) tamoxifen for postmenopausal women with estrogen receptor (ER)-positive breast cancer in 1982, (2) tamoxifen for premenopausal women diagnosed with ER-positive breast cancer in 1999, and (3) trastuzumab for women <60 years diagnosed with human epidermal growth factor receptor 2-positive breast cancer in 2007.</p><p><strong>Results: </strong>For the population-level effect of the introduction of tamoxifen among premenopausal women diagnosed with ER-positive breast cancer in 1999, the risk of recurrence decreased by nearly one-half (OR = 0.52), consistent with evidence from clinical trials; however, the estimate was imprecise (95% confidence interval [CI] = 0.25, 1.85). We observed an imprecise association between tamoxifen use and recurrence from the time it was introduced in 1982 (OR = 1.24 95% CI = 0.46, 5.11), inconsistent with prior knowledge from clinical trials. For the introduction of trastuzumab in 2007, the estimate was also consistent with trial evidence, though imprecise (OR = 0.51; 95% CI = 0.21, 22.4).</p><p><strong>Conclusions: </strong>We demonstrated how novel pharmacoepidemiologic analytic designs can be used to evaluate the routine clinical care and effectiveness of therapeutic advancements in a population-based setting while considering some limitations of the approach.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"35 5","pages":"660-666"},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-06-24DOI: 10.1097/EDE.0000000000001738
Christopher N Morrison, Christina F Mair, Lisa Bates, Dustin T Duncan, Charles C Branas, Brady R Bushover, Christina A Mehranbod, Ariana N Gobaud, Stephen Uong, Sarah Forrest, Leah Roberts, Andrew G Rundle
Background: Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology's current focus on causal inference and intervention.
Methods: We conducted a systematic review of studies indexed in PubMed that used the term "spatial epidemiolog*" in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content.
Results: A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2).
Conclusions: Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology.
{"title":"Defining Spatial Epidemiology: A Systematic Review and Re-orientation.","authors":"Christopher N Morrison, Christina F Mair, Lisa Bates, Dustin T Duncan, Charles C Branas, Brady R Bushover, Christina A Mehranbod, Ariana N Gobaud, Stephen Uong, Sarah Forrest, Leah Roberts, Andrew G Rundle","doi":"10.1097/EDE.0000000000001738","DOIUrl":"10.1097/EDE.0000000000001738","url":null,"abstract":"<p><strong>Background: </strong>Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology's current focus on causal inference and intervention.</p><p><strong>Methods: </strong>We conducted a systematic review of studies indexed in PubMed that used the term \"spatial epidemiolog*\" in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content.</p><p><strong>Results: </strong>A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2).</p><p><strong>Conclusions: </strong>Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"542-555"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140293237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-04-22DOI: 10.1097/EDE.0000000000001729
{"title":"Erratum: Average Causal Effect Estimation via Instrumental Variables: The No Simultaneous Heterogeneity Assumption.","authors":"","doi":"10.1097/EDE.0000000000001729","DOIUrl":"10.1097/EDE.0000000000001729","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"e15"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140862514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-06-24DOI: 10.1097/EDE.0000000000001747
Elsie M F Horne, William J Hulme, Edward P K Parker, Ruth H Keogh, Elizabeth J Williamson, Venexia M Walker, Tom M Palmer, Rachel Denholm, Rochelle Knight, Helen J Curtis, Alex J Walker, Colm D Andrews, Amir Mehrkar, Jessica Morley, Brian MacKenna, Sebastian C J Bacon, Ben Goldacre, Miguel A Hernán, Jonathan A C Sterne
Background: The UK delivered its first "booster" COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273.
Methods: With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1-14, 15-42, 43-69, 70-97, 98-126, 127-152, and 155-182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture.
Results: We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non-COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID-19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals.
Conclusions: We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes.
{"title":"Effectiveness of mRNA COVID-19 Vaccines as First Booster Doses in England: An Observational Study in OpenSAFELY-TPP.","authors":"Elsie M F Horne, William J Hulme, Edward P K Parker, Ruth H Keogh, Elizabeth J Williamson, Venexia M Walker, Tom M Palmer, Rachel Denholm, Rochelle Knight, Helen J Curtis, Alex J Walker, Colm D Andrews, Amir Mehrkar, Jessica Morley, Brian MacKenna, Sebastian C J Bacon, Ben Goldacre, Miguel A Hernán, Jonathan A C Sterne","doi":"10.1097/EDE.0000000000001747","DOIUrl":"10.1097/EDE.0000000000001747","url":null,"abstract":"<p><strong>Background: </strong>The UK delivered its first \"booster\" COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273.</p><p><strong>Methods: </strong>With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1-14, 15-42, 43-69, 70-97, 98-126, 127-152, and 155-182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture.</p><p><strong>Results: </strong>We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non-COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID-19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals.</p><p><strong>Conclusions: </strong>We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"35 4","pages":"568-578"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141442365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-06-24DOI: 10.1097/EDE.0000000000001748
{"title":"Marco Piccininni, Winner of the 2024 Rothman Prize.","authors":"","doi":"10.1097/EDE.0000000000001748","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001748","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"35 4","pages":"431"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141442366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-06-24DOI: 10.1097/EDE.0000000000001737
Ellicott C Matthay, Leyla M Mousli, Chloe Sun, Justin Lewis, Laurie M Jacobs, Stuart Heard, Raymond Ho, Laura A Schmidt, Dorie E Apollonio
Background: Cannabis exposures reported to the California Poison Control System increased following the initiation of recreational cannabis sales on 1 January 2018 (i.e., "commercialization"). We evaluated whether local cannabis control policies adopted by 2021 were associated with shifts in harmful cannabis exposures.
Methods: Using cannabis control policies collected for all 539 California cities and counties in 2020-2021, we applied a differences-in-differences design with negative binomial regression to test the association of policies with harmful cannabis exposures reported to California Poison Control System (2011-2020), before and after commercialization. We considered three policy categories: bans on storefront recreational retail cannabis businesses, overall restrictiveness, and specific recommended provisions (restricting product types or potency, packaging and labeling restrictions, and server training requirements).
Results: Localities that ultimately banned storefront recreational retail cannabis businesses had fewer harmful cannabis exposures for children aged <13 years (rate ratio = 0.82; 95% confidence interval = 0.65, 1.02), but not for people aged >13 years (rate ratio = 0.97; 95% confidence interval = 0.85, 1.11). Of 167 localities ultimately permitting recreational cannabis sales, overall restrictiveness was not associated with harmful cannabis exposures among children aged <13 years, but for people aged >13 years, a 1-standard deviation increase in ultimate restrictiveness was associated with fewer harmful cannabis exposures (rate ratio = 0.93; 95% confidence interval = 0.86, 1.01). For recommended provisions, estimates were generally too imprecise to detect associations with harmful cannabis exposures.
Conclusion: Bans on storefront retail and other restrictive approaches to regulating recreational cannabis may be associated with fewer harmful cannabis exposures for some age groups following statewide commercialization.
{"title":"Associations of Local Cannabis Control Policies With Harmful Cannabis Exposures Reported to the California Poison Control System.","authors":"Ellicott C Matthay, Leyla M Mousli, Chloe Sun, Justin Lewis, Laurie M Jacobs, Stuart Heard, Raymond Ho, Laura A Schmidt, Dorie E Apollonio","doi":"10.1097/EDE.0000000000001737","DOIUrl":"10.1097/EDE.0000000000001737","url":null,"abstract":"<p><strong>Background: </strong>Cannabis exposures reported to the California Poison Control System increased following the initiation of recreational cannabis sales on 1 January 2018 (i.e., \"commercialization\"). We evaluated whether local cannabis control policies adopted by 2021 were associated with shifts in harmful cannabis exposures.</p><p><strong>Methods: </strong>Using cannabis control policies collected for all 539 California cities and counties in 2020-2021, we applied a differences-in-differences design with negative binomial regression to test the association of policies with harmful cannabis exposures reported to California Poison Control System (2011-2020), before and after commercialization. We considered three policy categories: bans on storefront recreational retail cannabis businesses, overall restrictiveness, and specific recommended provisions (restricting product types or potency, packaging and labeling restrictions, and server training requirements).</p><p><strong>Results: </strong>Localities that ultimately banned storefront recreational retail cannabis businesses had fewer harmful cannabis exposures for children aged <13 years (rate ratio = 0.82; 95% confidence interval = 0.65, 1.02), but not for people aged >13 years (rate ratio = 0.97; 95% confidence interval = 0.85, 1.11). Of 167 localities ultimately permitting recreational cannabis sales, overall restrictiveness was not associated with harmful cannabis exposures among children aged <13 years, but for people aged >13 years, a 1-standard deviation increase in ultimate restrictiveness was associated with fewer harmful cannabis exposures (rate ratio = 0.93; 95% confidence interval = 0.86, 1.01). For recommended provisions, estimates were generally too imprecise to detect associations with harmful cannabis exposures.</p><p><strong>Conclusion: </strong>Bans on storefront retail and other restrictive approaches to regulating recreational cannabis may be associated with fewer harmful cannabis exposures for some age groups following statewide commercialization.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"35 4","pages":"447-457"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141442364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-05-06DOI: 10.1097/EDE.0000000000001742
Ashley L Buchanan, Raúl U Hernández-Ramírez, Judith J Lok, Sten H Vermund, Samuel R Friedman, Laura Forastiere, Donna Spiegelman
Background: Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery.
Methods: We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors.
Results: There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87).
Conclusions: These methods will be useful for evaluating intervention packages in studies with network features.
{"title":"Assessing Direct and Spillover Effects of Intervention Packages in Network-randomized Studies.","authors":"Ashley L Buchanan, Raúl U Hernández-Ramírez, Judith J Lok, Sten H Vermund, Samuel R Friedman, Laura Forastiere, Donna Spiegelman","doi":"10.1097/EDE.0000000000001742","DOIUrl":"10.1097/EDE.0000000000001742","url":null,"abstract":"<p><strong>Background: </strong>Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery.</p><p><strong>Methods: </strong>We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors.</p><p><strong>Results: </strong>There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87).</p><p><strong>Conclusions: </strong>These methods will be useful for evaluating intervention packages in studies with network features.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"481-488"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140848505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimating the instantaneous reproduction number ( ) in near real time is crucial for monitoring and responding to epidemic outbreaks on a daily basis. However, such estimates often suffer from bias due to reporting delays inherent in surveillance systems. We propose a fast and flexible Bayesian methodology to overcome this challenge by estimating while taking into account reporting delays. Furthermore, the method naturally takes into account the uncertainty associated with the nowcasting of cases to get a valid uncertainty estimation of the nowcasted reproduction number. We evaluate the proposed methodology through a simulation study and apply it to COVID-19 incidence data in Belgium.
{"title":"An Efficient Approach to Nowcasting the Time-varying Reproduction Number.","authors":"Bryan Sumalinab, Oswaldo Gressani, Niel Hens, Christel Faes","doi":"10.1097/EDE.0000000000001744","DOIUrl":"10.1097/EDE.0000000000001744","url":null,"abstract":"<p><p>Estimating the instantaneous reproduction number ( ) in near real time is crucial for monitoring and responding to epidemic outbreaks on a daily basis. However, such estimates often suffer from bias due to reporting delays inherent in surveillance systems. We propose a fast and flexible Bayesian methodology to overcome this challenge by estimating while taking into account reporting delays. Furthermore, the method naturally takes into account the uncertainty associated with the nowcasting of cases to get a valid uncertainty estimation of the nowcasted reproduction number. We evaluate the proposed methodology through a simulation study and apply it to COVID-19 incidence data in Belgium.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"512-516"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11191556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141093049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-05-20DOI: 10.1097/EDE.0000000000001746
Ryo Ikesu, Yingyan Wu, Scott C Zimmerman, Kosuke Inoue, Peter Buto, Melinda C Power, Catherine A Schaefer, M Maria Glymour, Elizabeth Rose Mayeda
Background: We evaluated whether participants in the landmark Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial represent US adults aged ≥40 with diabetes.
Methods: Using the nationally representative 2017-2020 prepandemic National Health and Nutrition Examination Survey data, we made operational definitions of ACCORD eligibility criteria. We calculated the percentage of individuals aged ≥40 with diabetes and HbA1c ≥ 6.0% or ≥ 7.5% who met operational ACCORD eligibility criteria.
Results: Applying survey sampling weights to 715 National Health and Nutrition Examination Survey participants aged ≥40 with diabetes and HbA1c ≥ 6.0% (representing 29,717,406 individuals), 12% (95% confidence interval [CI] = 8%, 18%) met the operational ACCORD eligibility criteria. Restricting to HbA1c ≥ 7.5%, 39% (95% CI = 28%, 51%) of respondents met the operational ACCORD eligibility criteria.
Conclusions: ACCORD represented a minority of US middle-aged and older adults with diabetes. Given the differential risk profile between ACCORD participants and the general population with diabetes, extrapolating the trial findings may not be appropriate.
{"title":"Representativeness of Participants in the ACCORD Trial Compared to Middle-aged and Older Adults Living with Diabetes in the United States.","authors":"Ryo Ikesu, Yingyan Wu, Scott C Zimmerman, Kosuke Inoue, Peter Buto, Melinda C Power, Catherine A Schaefer, M Maria Glymour, Elizabeth Rose Mayeda","doi":"10.1097/EDE.0000000000001746","DOIUrl":"10.1097/EDE.0000000000001746","url":null,"abstract":"<p><strong>Background: </strong>We evaluated whether participants in the landmark Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial represent US adults aged ≥40 with diabetes.</p><p><strong>Methods: </strong>Using the nationally representative 2017-2020 prepandemic National Health and Nutrition Examination Survey data, we made operational definitions of ACCORD eligibility criteria. We calculated the percentage of individuals aged ≥40 with diabetes and HbA1c ≥ 6.0% or ≥ 7.5% who met operational ACCORD eligibility criteria.</p><p><strong>Results: </strong>Applying survey sampling weights to 715 National Health and Nutrition Examination Survey participants aged ≥40 with diabetes and HbA1c ≥ 6.0% (representing 29,717,406 individuals), 12% (95% confidence interval [CI] = 8%, 18%) met the operational ACCORD eligibility criteria. Restricting to HbA1c ≥ 7.5%, 39% (95% CI = 28%, 51%) of respondents met the operational ACCORD eligibility criteria.</p><p><strong>Conclusions: </strong>ACCORD represented a minority of US middle-aged and older adults with diabetes. Given the differential risk profile between ACCORD participants and the general population with diabetes, extrapolating the trial findings may not be appropriate.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"432-436"},"PeriodicalIF":4.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141075020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}