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Supplemental Digital Content is available in the text., Background: The use of Prescription Drug Monitoring Program (PDMP) data has greatly increased in recent years as these data have accumulated as part of the response to the opioid epidemic in the United States. We evaluated the accuracy of record linkage approaches using the Controlled Substance Monitoring Database (Tennessee’s [TN] PDMP, 2012–2016) and mortality data on all drug overdose decedents in Tennessee (2013–2016). Methods: We compared total, missed, and false positive (FP) matches (with manual verification of all FPs) across approaches that included a variety of data cleaning and matching methods (probabilistic/fuzzy vs. deterministic) for patient and death linkages, and prescription history. We evaluated the influence of linkage approaches on key prescription measures used in public health analyses. We evaluated characteristics (e.g., age, education, sex) of missed matches and incorrect matches to consider potential bias. Results: The most accurate probabilistic/fuzzy matching approach identified 4,714 overdose deaths (vs. the deterministic approach, n = 4,572), with a low FP linkage error (

Significance Forecasts routinely provide critical information for dangerous weather events but not yet for epidemics. Researchers develop computational models that can be used for infectious disease forecasting, but forecasts have not been broadly compared or tested. We collaboratively compared forecasts from 16 teams for 8 y of dengue epidemics in Peru and Puerto Rico. The comparison highlighted components that forecasts did well (e.g., situational awareness late in the season) and those that need more work (e.g., early season forecasts). It also identified key facets to improve forecasts, including using multiple model ensemble approaches to improve overall forecast skill. Future infectious disease forecasting work can build on these findings and this framework to improve the skill and utility of forecasts., A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project—integration with public health needs, a common forecasting framework, shared and standardized data, and open participation—can help advance infectious disease forecasting beyond dengue.

BACKGROUND: In time-to-event analyses, there is limited guidance on when persons who are lost to follow-up (LTFU) should be censored. METHODS: We simulated bias in risk estimates for: (1) a composite event of measured (outcome only observable in a patient encounter) and captured events (outcome observable outside a patient encounter); and a (2) measured or (3) captured event in the presence of a competing event of the other type, under three censoring strategies: (i) censor at the last study encounter; (ii) censor when LTFU definition is met; and (iii) a new, hybrid censoring strategy. We demonstrate the real-world impact of this decision by estimating: (1) time to Acquired Immune Deficiency Syndrome diagnosis (AIDS) or death, (2) time to initiation of antiretroviral therapy (ART), and (3) time to death prior to ART initiation among adults engaged in HIV care. RESULTS: For (1) our hybrid censoring strategy was least biased. In our example, 5-year risk of AIDS or death was over-estimated using last-encounter censoring (25%) and under-estimated using LTFU-definition censoring (21%), compared to results from our hybrid approach (24%). Last-encounter censoring was least biased for (2). When estimating 5-year risk of ART initiation, LTFU-definition censoring underestimated risk (80% versus 85% using last-encounter censoring). LTFU-definition censoring was least biased for (3). When estimating 5-year risk of death before ART initiation, last-encounter censoring overestimated risk (5.2% versus 4.7% using LTFU-definition censoring). CONCLUSIONS: The least biased censoring strategy for time-to-event analyses in the presence of LTFU depends on the event and estimand of interest.

Background: Effective antiretroviral therapy (ART) has increased life expectancy for HIV patients to levels close to that observed for general population. Comorbidities are also increasing, due to ageing of such population. Erectile dysfunction (ED) has been a frequent finding in men living with HIV. Objectives: The goals of the present study were to define the prevalence of ED and associated factors in HIV-infected men, in a referral center of Salvador city, Brazil. Methods: HIV-infected men ≥18 years of age, receiving care at the AIDS Clinics of Complexo Hospitalar Professor Edgard Santos (C-HUPES), Universidade Federal da Bahia were consecutively included in the study until the estimated sample size (N = 134) was reached. Patients filled a structured questionnaire on clinical-epidemiological characteristics, as well as the International Index on Erectile Function to assess ED. Results: Most (55%) were black and single (79%), mean age 44.8 years. ED was detected in 29 (21.6%) patients; 86% of ED cases were classified as severe. The only factors associated with ED were low income (p = 0.02) and unemployment (p = 0.01). No association was found between ED and frequency of comorbidities, age, or ART used by patients. Conclusion: In a referral center in Salvador city, socio-economic problems were the main factors associated with ED. Psychological impact of poor social conditions should be routinely assessed in patients with ED to implement preventive measures for ED. Keywords: Erectile dysfunction, AIDS, Bahia, Risk factors

Background With expanding recreational cannabis legalisation, pregnant women and their offspring are at risk of potentially harmful consequences. Objectives To assess the prevalence of recreational cannabis use among pregnant women, health outcomes associated with prenatal recreational cannabis use, and the potential impact of recreational cannabis legalisation on this population. Search strategy Five databases and the grey literature were systematically searched (2000-2019). Selection criteria Human studies published in English or French reporting on the prevalence of prenatal recreational cannabis use in high-income countries. Data collection and analysis Data on study characteristics, prenatal substance use, and health outcomes were extracted and qualitatively synthesised. Main results Forty-one publications met our inclusion criteria. The overall prevalence of prenatal cannabis use varied substantially (min-max: 0.24-22.6%), with the greatest use in the first trimester. In the three studies with temporal data available, rates of prenatal cannabis use increased across years. Only 7/41 and 5/41 studies provided information on gestational age of exposure and frequency of use, respectively. The concomitant use of alcohol, illicit drugs, and tobacco was higher among cannabis users than nonusers. Prenatal cannabis use was associated with select neonatal, but not maternal, health outcomes. There were insufficient data to compare prenatal cannabis use between the pre- and post-legalisation periods. Conclusion Cannabis use among pregnant women is prevalent and may be associated with adverse neonatal outcomes. Future studies should assess the gestational age and frequency of cannabis exposure, and usage patterns prior to and following legalisation. Tweetable abstract Women who consume cannabis during pregnancy could risk predisposing their newborns to poor birth outcomes.

Objectives: By adopting a new method, this study aimed to analyse the epidemiological characteristics of hand, foot, and mouth disease (HFMD) in nine districts and counties (cities) of Changsha Cit...

Background Enhanced recovery after surgery (ERAS) represents an evidence-based multidisciplinary approach to perioperative management after major surgery that decreases complications and readmissions and improves functional recovery. Spine surgery is a traditionally invasive intervention with an extended recovery phase and may benefit from ERAS protocol integration. Methods We analyzed the use of ERAS in spine surgery by completing a search using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the PICOS (Participants, Intervention, Comparison, Outcomes, Study Design) model through PubMed and Ovid databases to identify studies that fit our search criteria. We assess the outcomes and ERAS elements selected across protocols as well as the study design and internal validation methods. Results A total of 19 studies met the inclusion criteria and were used in our analysis. Patient populations differed significantly across all 4 studies. Reduction in length of stay was reported in 7 studies using the ERAS protocol. Comparative studies between ERAS and non-ERAS show improved pain scores and reduced opioid consumption postoperatively, but no differences in complications or readmissions between groups. Complication rates under ERAS protocols ranged from 2.0% to 31.7%. Significant pain reduction in visual analog scale scores was observed with 3 ERAS protocols. Direct, indirect, and total cost decreases were also observed with implementation of ERAS protocols. Conclusions A limited cohort of studies with significant variability in patient population and ERAS protocol implementation have evaluated the integration of ERAS within spine surgery. ERAS in spine surgery may provide reductions in complications, readmissions, length of stay, and opioid use, in combination with improvements in patient-reported outcomes and functional recovery.

Could there be unexpected similarities between different studies, diseases, or treatments, on a molecular level due to common biological mechanisms involved? To answer this question, we develop a method for computing similarities between empirical, statistical distributions of high-dimensional, low-sample datasets, and apply it on hundreds of -omics studies. The similarities lead to dataset-to-dataset networks visualizing the landscape of a large portion of biological data. Potentially interesting similarities connecting studies of different diseases are assembled in a disease-to-disease network. Exploring it, we discover numerous non-trivial connections between Alzheimer’s disease and schizophrenia, asthma and psoriasis, or liver cancer and obesity, to name a few. We then present a method that identifies the molecular quantities and pathways that contribute the most to the identified similarities and could point to novel drug targets or provide biological insights. The proposed method acts as a “statistical telescope” providing a global view of the constellation of biological data; readers can peek through it at: http://datascope.csd.uoc.gr:25000/., Statistics reveal molecular sources of disease similarities Different phenotypes (e.g., diseases) may be distinct on a macroscopic level, however, they may share several common biological mechanisms on a molecular level. The molecular similarities should be reflected as statistical similarities in corresponding -omics data measurements. A method is presented to identify these statistical similarities. It is then applied to hundreds of public -omics datasets stemming from different studies, creating a network of diseases (phenotypes in general) whose statistical molecular patterns are similar. We discover numerous non-trivial connections on a molecular level between Alzheimer's disease and schizophrenia, asthma and psoriasis, liver cancer and obesity, to name a few. The quantities and pathways that contribute the most to the identified similarities are also reported. They could point to novel drug targets or biological insights.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Medicina Interna, 2020: Publicação Especial - COVID 19

Wenxian Sun,1 Luyang Zhang,1 Weishi Liu,1 Mengke Tian,1 Xin Wang,1 Jing Liang,1 Yuying Wang,1 Lan Ding,1 Lulu Pei,1 Jie Lu,2 Yuming Xu,1 Bo Song1 1Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People’s Republic of China; 2Department of Epidemiology and Health Statistics College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, People’s Republic of ChinaCorrespondence: Bo SongDepartment of Neurology, The First Affiliated Hospital of Zhengzhou University, 1st Jianshe Eastern Road, Zhengzhou, 450052, Henan Province, People’s Republic of ChinaTel +86-13603983297Fax +86-371-66975140Email fccsongb@zzu.edu.cnBackground: Stroke and myocardial infarction (MI) are associated with each other, as demonstrated in observational studies. However, it is unclear whether this relationship is causal, and the purpose of this study was to explore the bidirectional causality between stroke and MI.Methods: Causality between stroke and MI was assessed using two-sample Mendelian randomization (MR). All genetic instruments related to stroke (40,585 cases; 406,111 controls) and MI (43,676 cases; 128,199 controls) were derived from large published genome-wide association study. The MR analysis was calculated with inverse-variance weighting, MR-Egger, weighted mode, weighted median, and simple mode methods, and sensitivity analyses are used to detect the heterogeneity or pleiotropy.Results: Genetically predicted large-artery stroke (LAS) was causally related to higher odds of MI (odds ratio [OR] = 1.13, 95% confidence interval [CI]: 1.06– 1.20, p = 1.0× 10− 4), and the causal effect of LAS on MI was significantly weakened (OR = 1.09, 95% CI: 1.02– 1.17, p = 0.017) after excluding the multipotent single-nucleotide polymorphisms (SNPs). MI phenotypes were genetically correlated with all ischemic strokes (OR = 1.15, 95% CI: 1.03– 1.28, p = 0.013) and LAS (OR = 1.39, 95% CI: 1.14– 1.71, p = 0.001); but a causal effect of MI on all ischemic strokes (OR = 1.00, 95% CI: 0.95– 1.28, p = 0.219) and LAS (OR = 1.26, 95% CI: 0.93– 1.69, p = 0.130) was not observed after excluding the multipotent SNPs.Conclusion: This MR analysis provides evidence to support the causal effect of LAS subtype on MI, and some factors act as confiding factors whereas others may act as mediators.Keywords: causal inference, single-nucleotide polymorphism, genome-wide association study, epidemiologic methods, large-artery stroke

Significance The US COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. As the largest public health survey conducted in the United States to date, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid revision to address emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing, and vaccination in relation to evolving high-priority policy questions over 12 mo of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting., The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Significance The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 2020, is the largest remote global health monitoring system. This study includes ∼30 million responses through December 2020 from all 114 countries/territories with survey weights to adjust for nonresponse and demographics. Using self-reported cross-sectional survey data sampled daily from Facebook users, we confirm consistent demographics and COVID-19 symptoms. Our global model predicts local COVID-19 case trends. Importantly, one survey item strongly correlates with reported cases, demonstrating potential utility in locales with scant UMD-CTIS sampling or government data. Despite limitations resulting from sampling, nonresponse, coverage, and measurement error, UMD-CTIS has the potential to support existing monitoring systems for COVID-19 as well as other new as-yet-undefined global health threats., Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Background Most immune-related adverse event (irAE) patterns and treatment guidelines are based on western clinical data. We evaluated the incidence and patterns of irAEs in patients treated with immune-checkpoint inhibitors (ICI) in Thailand. Methods All solid tumor patients treated with ICIs were retrospectively reviewed in a multicenter analysis. The study aims to evaluate the incidence of irAEs and their characteristics, treatments, outcomes, and impact on survival. All irAEs were graded using the CTCAE version 4.0. Characteristics of irAEs including time to onset, duration of irAEs, specific treatments, and outcomes of irAEs were reviewed. The Chi-square or Fisher’s exact test was used to compare variables. Overall survival (OS) was estimated by the Kaplan-Meier method, and compared by the log-rank test. A p-value Results irAEs of any grade were observed in 98 of 414 patients (24%), whereas grades 3–4 irAEs were observed in 5.6%. The majority of patients (78%) were treated with monotherapy ICI (anti-PD1/PD-L1 92%). The most common all-grade irAEs were hypothyroidism (7.5%), hepatitis (6.5%), and rash (4.8%). Median onset of overall irAEs was 63 days. Pancreatitis and pneumonitis had the earliest onset at 30 and 34 days, respectively. ICIs were rechallenged in 68 of 98 patients with irAE. Eleven of sixty-eight patients (11.2%) with initial irAE had reoccurrence after ICI rechallenge. Based on a multivariate analysis, pre-existing hypothyroidism, ICI used in a clinical trial setting, and combinations of ICI/ICI were independent factors predicting irAE occurrence. Patients with irAE had a statistically significant longer overall survival (OS) when compared to patients without irAE (p = 0.019). A multivariate analysis revealed that occurrence of irAE was an independent prognostic factor for OS (HR 0.70, 95% CI 0.51–0.96; p = 0.028). Conclusion irAE was commonly observed in Thai cancer patients treated with ICIs. Most irAEs were low-grade and manageable. Re-occurrence of irAE after re-challenging ICI was not uncommonly observed. Patients who experienced irAEs might have significantly longer OS compared to patients without irAEs. However, OS in this study should be interpreted with caution since it might be affected by various tumor types, treatment settings, dosing schedule, and ICI combinations.

Resumo O objetivo foi descrever as reclamações de idosos brasileiros recebidas pela Agência Nacional de Saúde Suplementar. Trata-se de um estudo ecológico, com dados secundários provenientes do Sistema Integrado de Fiscalização. Realizou-se análise de tendência das reclamações recebidas pela Agência Nacional de Saúde Suplementar de beneficiários idosos de todo o Brasil, no período de 2008 a 2017. Analisaram-se 111.497 reclamações realizadas por idosos beneficiários da saúde suplementar. Identificou-se aumento das taxas de reclamações para todas as regiões do país, com destaque para a região Nordeste, que apresentou a maior taxa média de reclamações (27,07) e o maior aumento médio anual (7,79; r2=0,97), e a região Sul, com a menor taxa média para o período (7,11) e o menor aumento médio anual observado (1,84; r2=0,92). A maior taxa de reclamações foi relacionada à cobertura do plano, entre idosos de 70 a 79 anos e beneficiários de planos individuais ou familiares. A crescente insatisfação de idosos com o sistema de saúde suplementar refletiu no grande número de reclamações recebidas pela Agência Nacional de Saúde Suplementar ao longo de nove anos. Abstract The scope of this study was to describe the complaints of elderly Brazilians received by the National Supplemental Health Agency. It is an ecological study, with secondary data from the Integrated Inspection System. A trend analysis of the complaints received by the National Supplemental Health Agency of elderly beneficiaries from all over Brazil was conducted between 2008 and 2017. A total of 111,497 complaints were filed by elderly beneficiaries of the supplemental health system. The highest number of complaints (27.07) and the highest average annual increase (7.79, r2=0, 97), and the South region, with the lowest average rate for the period (7.11) and the lowest observed mean annual increase (1.84; r2=0.92) were identified. The highest rate of complaints was related to the coverage of the plan, between the elderly aged 70 to 79 and beneficiaries of individual or family plans. Growing dissatisfaction among older people with the supplemental health system reflected the large number of complaints received by the National Supplemental Health Agency over a period of nine years.

Significance Validated forecasting methodology should be a vital element in the public health response to any fast-moving epidemic or pandemic. A widely used model for predicting the future spread of a temporal process is an autoregressive (AR) model. While basic, such an AR model (properly trained) is already competitive with the top models in operational use for COVID-19 forecasting. In this paper, we exhibit five auxiliary indicators—based on deidentified medical insurance claims, self-reported symptoms via online surveys, and COVID-related Google searches—that further improve the predictive accuracy of an AR model in COVID-19 forecasting. The most substantial gains appear to be in quiescent times; but the Google search indicator appears to also offer improvements during upswings in pandemic activity., Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

Objective: To evaluate the accuracy of Virtual Surgical Planning (VSP) comparing VSPs and post-operative CBCT scans in patients undergoing bimaxillary orthognathic surgery of severe Skeletal Class III malocclusion. Material and Methods: Twenty-three patients (9 males and 14 females, mean age 24.1 ± 7.0 years) were selected and submitted to bimaxillary orthognathic surgery. Pre-operative VSPs and post-operative CBCTs were compared using both linear (taking into account four skeletal and six dental landmarks, each one described by the respective coordinates) and angular measures (seven planes in total). The threshold discrepancies for post-operative clinical acceptable results were set at ≤2 mm for liner and ≤4° for angular discrepancies. The mean difference values and its 95% confidence interval were identified, comparing which planned and which obtained in absolute value. Results: There were significant statistical differences for all absolute linear measures investigated, although only two overcome the linear threshold value of 2mm in both X and Y-linear dimensions. Linear deviations in Z-linear dimension do not reach statistical significance. All 12 angular measures reach the statistical significance, although none overcome the threshold angular value of 4°. Angular deviation for roll register the higher accuracy in contrast to pitch and yaw. Conclusion: Virtual surgical planning is a reliable planning method to be used in orthognathic surgery field; as a matter of fact, although some discrepancies between the planned on the obtained are evident, most of them meet the tolerability range.

Use of computed tomography (CT) scanning has increased substantially since its introduction in the 1990s. Several authors have reported increased risk of leukemia and brain tumors associated with radiation exposure from CT scans. However, reverse causation is a concern, particularly for brain cancer; in other words, the CT scan may have been taken because of preexisting cancer and therefore not have been a cause. We assessed the possibility of reverse causation via a simulation study focused on brain tumors, using a simplified version of the data structure for recent CT studies. Five-year–lagged and unlagged analyses implied an observed excess risk per scan up to 70% lower than the true excess risk per scan, particularly when more than 10% of persons with latent cancer had increased numbers of scans or the extra scanning rate after development of latent cancer was greater than 2 scans/year; less extreme values of these parameters imply little risk attenuation. Without a lag and when more than 20% of persons with latent cancer had increased scans—an arguably implausible scenario—the excess risk per scan was increased over the true excess risk per scan by up to 35%–40%. This study suggests that with a realistic lag, reverse causation results in downwardly biased risk, a result of induced classical measurement error, and is therefore unlikely to produce a spurious positive association between cancer and radiation dose from CT scans.

In their article, Yland et al. (Am J Epidemiol. 2022;191(8):1485–1495) bring renewed attention to the oft-cited heuristic “nondifferential misclassification biases results towards the null.” They make a compelling case, as others have before, that this heuristic is misguided. Herein, I briefly discuss the history of this heuristic, discuss why I believe it became and stayed popular, and note the very limited circumstances for which it may be appropriate.

Background: The SARS-CoV-2 pandemic has highlighted the risk of infection transmission in long-term care facilities (LTCF) and the vulnerability of resident populations. It is essential to understand the environmental spread of the virus and risk of indirect transmission to inform Infection Prevention and Control (IPC) measures in these settings.Methods: Upon notification of SARS-CoV-2 outbreaks, LTCF within a local authority in the South West of England were approached to take part in this pilot study. Investigators visited to swab common touch-points and elevated ‘non-touch’ surfaces and samples were analysed for presence of SARS-CoV-2 genetic material (RNA). Data were collected regarding LTCF infrastructure, staff behaviours, clinical and epidemiological risk factors for infection (staff and residents), and IPC measures.Criteria for success were: recruitment of three LTCF; detection of SARS-COV-2 RNA; variation in proportion of SARS-CoV-2 positive surfaces by sampling zone; potential to assess infection risk from SARS-CoV-2 positive surfaces.Results: Three LTCFs were recruited, ranging in size and resident demographics. Outbreaks lasted 63, 50 and 30 days with resident attack rates of 53%, 40% and 8%, respectively. The proportion of sample sites on which SARS-CoV-2 was detected was highest in rooms occupied by infected residents and varied elsewhere in the LTCF, with low levels in a facility implementing enhanced IPC measures. The heterogeneity of settings and difficulty obtaining data made it difficult to assess association between environmental contamination and infection. Elevated surfaces were more likely to test positive for SARS-CoV-2 RNA than common touch-points.Conclusions: SARS-CoV-2 RNA can be detected in a variety of LTCF outbreak settings. We identified variation in environmental spread which could be associated with implementation of IPC measures, though we were unable to assess the impact on infection risk. Sampling elevated surfaces could add to ongoing public health surveillance for SARS-CoV-2 and other airborne pathogens in LTCF.

Dynamical models, commonly used in infectious disease epidemiology, are formal mathematical representations of time-changing systems or processes. For many chronic disease epidemiologists, the link between dynamical models and predominant causal inference paradigms is unclear. In this commentary, we explain the use of dynamical models for representing causal systems and the relevance of dynamical models for causal inference. In certain simple settings, dynamical modeling and conventional statistical methods (e.g., regression-based methods) are equivalent, but dynamical modeling has advantages over conventional statistical methods for many causal inference problems. Dynamical models can be used to transparently encode complex biological knowledge, interference and spillover, effect modification, and variables that influence each other in continuous time. As our knowledge of biological and social systems and access to computational resources increases, there will be growing utility for a variety of mathematical modeling tools in epidemiology.

Peste des petits ruminants (PPR) is a highly contagious and devastating viral disease infecting predominantly sheep and goats. Tracking outbreaks of disease and analysing the movement of the virus often involves sequencing part or all of the genome and comparing the sequence obtained with sequences from other outbreaks, obtained from the public databases. However, there are a very large number (>1800) of PPRV sequences in the databases, a large majority of them relatively short, and not always well-documented. There is also a strong bias in the composition of the dataset, with countries with good sequencing capabilities (e.g. China, India, Turkey) being overrepresented, and most sequences coming from isolates in the last 20 years. In order to facilitate future analyses, we have prepared sets of PPRV sequences, sets which have been filtered for sequencing errors and unnecessary duplicates, and for which date and location information has been obtained, either from the database entry or from other published sources. These sequence datasets are freely available for download, and include smaller datasets which maximise phylogenetic information from the minimum number of sequences, and which will be useful for simple lineage identification. Their utility is illustrated by uploading the data to the MicroReact platform to allow simultaneous viewing of lineage date and geographic information on all the viruses for which we have information. While preparing these datasets, we identified a significant number of public database entries which contain clear errors, and propose guidelines on checking new sequences and completing metadata before submission.

Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification.

Background Critical knowledge of what we know about health and disease, risk factors, causation, prevention, and treatment, derives from epidemiology. Unfortunately, its methods and language can be misused and improperly applied. A repertoire of methods, techniques, arguments, and tactics are used by some people to manipulate science, usually in the service of powerful interests, and particularly those with a financial stake related to toxic agents. Such interests work to foment uncertainty, cast doubt, and mislead decision makers by seeding confusion about cause-and-effect relating to population health. We have compiled a toolkit of the methods used by those whose interests are not aligned with the public health sciences. Professional epidemiologists, as well as those who rely on their work, will thereby be more readily equipped to detect bias and flaws resulting from financial conflict-of-interest, improper study design, data collection, analysis, or interpretation, bringing greater clarity—not only to the advancement of knowledge, but, more immediately, to policy debates. Methods The summary of techniques used to manipulate epidemiological findings, compiled as part of the 2020 Position Statement of the International Network for Epidemiology in Policy (INEP) entitled Conflict-of-Interest and Disclosure in Epidemiology, has been expanded and further elucidated in this commentary. Results Some level of uncertainty is inherent in science. However, corrupted and incomplete literature contributes to confuse, foment further uncertainty, and cast doubt about the evidence under consideration. Confusion delays scientific advancement and leads to the inability of policymakers to make changes that, if enacted, would—supported by the body of valid evidence—protect, maintain, and improve public health. An accessible toolkit is provided that brings attention to the misuse of the methods of epidemiology. Its usefulness is as a compendium of what those trained in epidemiology, as well as those reviewing epidemiological studies, should identify methodologically when assessing the transparency and validity of any epidemiological inquiry, evaluation, or argument. The problems resulting from financial conflicting interests and the misuse of scientific methods, in conjunction with the strategies that can be used to safeguard public health against them, apply not only to epidemiologists, but also to other public health professionals. Conclusions This novel toolkit is for use in protecting the public. It is provided to assist public health professionals as gatekeepers of their respective specialty and subspecialty disciplines whose mission includes protecting, maintaining, and improving the public’s health. It is intended to serve our roles as educators, reviewers, and researchers.

OBJECTIVES We aimed to reassess the relationship between phototherapy and cancer in an extended version of a previous cohort and to replicate a report from Quebec of increased cancer risk after phototherapy beginning at age 4 years. METHODS This cohort study included 139 100 children born at ≥35 weeks’ gestation from 1995 to 2017, followed through March 16, 2019, in Kaiser Permanente Northern California hospitals who had a qualifying bilirubin level from −3 mg/dL to +4.9 mg/dL from the American Academy of Pediatrics phototherapy threshold; an additional 40 780 children and 5 years of follow-up from our previous report. The exposure was inpatient phototherapy (yes or no), and the outcomes were various types of childhood cancer. We used Cox proportional hazard models, controlling for propensity-score quintiles, and allowed for time-dependent exposure effects to assess for the risk of cancer after a latent period. RESULTS Over a mean (SD) follow-up of 8.2 (5.7) years, the crude incidence of cancer per 100 000 person-years was 25.1 among those exposed to phototherapy and 19.2 among those not exposed (233 cases of cancer). After propensity adjustment, phototherapy was not associated with any cancer (hazard ratio [HR]: 1.13, 95% confidence interval [CI]: 0.83–1.54), hematopoietic cancer (HR: 1.17, 95% CI: 0.74–1.83), or solid tumors (HR: 1.01, 95% CI: 0.65–1.58). We also found no association with cancer diagnoses at age ≥4 years. CONCLUSIONS We did not confirm previous, concerning associations between phototherapy and adjusted risk of any cancer, nonlymphocytic leukemia, or brain and/or central nervous systems tumors in later childhood.

OBJECTIVES In this study, we sought to identify which adolescents progress to regular electronic cigarette (e-cigarette) use (without cigarette smoking), which adolescents become dual users of both types of cigarettes, and how dual use develops across time. METHODS Adolescents (N = 1808) from public high schools outside Philadelphia, Pennsylvania, completed in-classroom surveys at wave 1 (fall 2016, beginning of ninth grade) and at 6-month intervals for the following 36 months (fall 2019, beginning of 12th grade). RESULTS A sequential processes growth mixture model identified 4 conjoint latent classes: later, rapid e-cigarette uptake (class 1: n = 230); no use of e-cigarettes or combustible cigarettes (class 2: n = 1141); earlier, steady e-cigarette uptake (class 3: n = 265); and dual use of e-cigarettes and combustible cigarettes (class 4: n = 204). Using a rich set of potential risk factors, multinomial logistic regression assessed the likelihood of belonging to each conjoint class compared with the comparison class (dual use). Adolescents in the dual use class were characterized by a greater number and severity of e-cigarette and combustible cigarette risk factors. Adolescents in the 2 e-cigarettes–only classes were characterized by either e-cigarette–specific risk factors (earlier onset) or no risk factors (later onset). The no use class had an absence of risk factors for e-cigarette and cigarette use. CONCLUSIONS This study provides new prospective evidence for distinct patterns and profiles of adolescents who progress to current e-cigarette use, including adolescents who were initially cigarette smokers. The findings have implications for prevention intervention timing, tobacco product focus, content, and the adolescent subgroups to target.