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The association between traumatic brain injury (TBI) and risk for Alzheimer disease and related dementias (ADRD) has been investigated in multiple studies, yet reported effect sizes have varied widely. Large differences in comorbid and demographic characteristics between individuals with and without TBI could result in spurious associations between TBI and poor outcomes, even when control for confounding is attempted. Yet, inadvertent control for post-TBI exposures (e.g., psychological and physical trauma) could result in an underestimate of the effect of TBI. Choice of the unexposed or comparison group is critical to estimating total associated risk. The objective of this study was to highlight how selection of the comparison group impacts estimates of the effect of TBI on risk for ADRD. Using data on Veterans aged ≥55 years obtained from the Veterans Health Administration (VA) for years 1999–2019, we compared risk of ADRD between Veterans with incident TBI (n = 9440) and (1) the general population of Veterans who receive care at the VA (All VA) (n = 119,003); (2) Veterans who received care at a VA emergency department (VA ED) (n = 111,342); and (3) Veterans who received care at a VA ED for non-TBI trauma (VA ED NTT) (n = 65,710). In inverse probability of treatment weighted models, TBI was associated with increased risk of ADRD compared with All VA (hazard ratio [HR] 1.94; 95% confidence interval [CI] 1.84, 2.04), VA ED (HR 1.42; 95% CI 1.35, 1.50), and VA ED NTT (HR 1.12; 95% CI 1.06, 1.18). The estimated effect of TBI on incident ADRD was strongly impacted by choice of the comparison group. [ABSTRACT FROM AUTHOR]

Background: Burden disease related to chronic HBV infection is increasing worldwide. Monitoring Hepatitis B occurrence is difficult due to intrinsic characteristics of the infection, nonetheless analyzing this information improves strategic planning towards reducing the burden related to chronic infection. In this line of thought, this study aims to analyze national and regional epidemiology of Hepatitis B and it's temporal trends based on Brazilian reported cases.Methods: Data obtained from the Brazilian National Notifiable Disease Reporting System (SINAN) from 2007 to 2018 were classified by infection status with an original classification algorithm, had their temporal trends analyzed by Joinpoint regression model and were correlated with gender, age and region.Results: Of the 487,180 hepatitis B cases notified to SINAN, 97.65% had it infection status correctly classified by the new algorithm. Hepatitis B detection rate, gender and age-distribution were different among Brazilian regions. Overall, detection rates remained stable from 2007 to 2018, achieving their maximal value (56.1 cases per 100,000 inhabitants) in North region. However, there were different temporal trends related to different hepatitis B status and age. Women mean age at notification were always inferior to those of men and the difference was higher in Central-West, North and Northeast regions.Conclusion: Hepatitis B affects heterogeneously different populations throughout Brazilian territory. The differences shown in its temporal trends, regional, gender and age-related distribution helps the planning and evaluation of control measures in Brazil. [ABSTRACT FROM AUTHOR]

Background: The incidence of sepsis can be estimated between 250 and 500 cases/100.000 people per year and is responsible for up to 6% of total hospital admissions. Identified as one of the most relevant global health problems, sepsis is the condition that generates the highest costs in the healthcare system. Important changes in the management of septic patients have been included in recent years; however, there is no information about how changes in the management of sepsis-associated organ failure have contributed to reduce mortality.Methods: A retrospective analysis was conducted from hospital discharge records from the Minimum Basic Data Set Acute-Care Hospitals (CMBD-HA in Catalan language) for the Catalan Health System (CatSalut). CMBD-HA is a mandatory population-based register of admissions to all public and private acute-care hospitals in Catalonia. Sepsis was defined by the presence of infection and at least one organ dysfunction. Patients hospitalized with sepsis were detected, according ICD-9-CM (since 2005 to 2017) and ICD-10-CM (2018 and 2019) codes used to identify acute organ dysfunction and infectious processes.Results: Of 11.916.974 discharges from all acute-care hospitals during the study period (2005-2019), 296.554 had sepsis (2.49%). The mean annual sepsis incidence in the population was 264.1 per 100.000 inhabitants/year, and it increased every year, going from 144.5 in 2005 to 410.1 in 2019. Multiorgan failure was present in 21.9% and bacteremia in 26.3% of cases. Renal was the most frequent organ failure (56.8%), followed by cardiovascular (24.2%). Hospital mortality during the study period was 19.5%, but decreases continuously from 25.7% in 2005 to 17.9% in 2019 (p < 0.0001). The most important reduction in mortality was observed in cases with cardiovascular failure (from 47.3% in 2005 to 31.2% in 2019) (p < 0.0001). In the same way, mean mortality related to renal and respiratory failure in sepsis was decreased in last years (p < 0.0001).Conclusions: The incidence of sepsis has been increasing in recent years in our country. However, hospital mortality has been significantly reduced. In septic patients, all organ failures except liver have shown a statistically significant reduction on associated mortality, with cardiovascular failure as the most relevant. [ABSTRACT FROM AUTHOR]

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007–2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities. [ABSTRACT FROM AUTHOR]

Abstract Background Immortal time bias is common in observational studies but is typically described for pharmacoepidemiology studies where there is a delay between cohort entry and treatment initiation. Methods This study used the Clinical Practice Research Datalink (CPRD) and linked national mortality data in England from 2000 to 2019 to investigate immortal time bias for a specific life-long condition, intellectual disability. Life expectancy (Chiang’s abridged life table approach) was compared for 33,867 exposed and 980,586 unexposed individuals aged 10+ years using five methods: (1) treating immortal time as observation time; (2) excluding time before date of first exposure diagnosis; (3) matching cohort entry to first exposure diagnosis; (4) excluding time before proxy date of inputting first exposure diagnosis (by the physician); and (5) treating exposure as a time-dependent measure. Results When not considered in the design or analysis (Method 1), immortal time bias led to disproportionately high life expectancy for the exposed population during the first calendar period (additional years expected to live: 2000–2004: 65.6 [95% CI: 63.6,67.6]) compared to the later calendar periods (2005–2009: 59.9 [58.8,60.9]; 2010–2014: 58.0 [57.1,58.9]; 2015–2019: 58.2 [56.8,59.7]). Date of entry of diagnosis (Method 4) was unreliable in this CPRD cohort. The final methods (Method 2, 3 and 5) appeared to solve the main theoretical problem but residual bias may have remained. Conclusions We conclude that immortal time bias is a significant issue for studies of life-long conditions that use electronic health record data and requires careful consideration of how clinical diagnoses are entered onto electronic health record systems.

Dahai Yu,1 Matthew Missen,1 Kelvin P Jordan,1 John J Edwards,1 James Bailey,1 Ross Wilkie,1 Justine Fitzpatrick,2 Nuzhat Ali,2 Paul Niblett,2 George Peat1 1Primary Care Centre versus Arthritis, School of Medicine, Keele University, Keele, Staffordshire, UK; 2Office for Health Improvement and Disparities, Department of Health and Social Care, London, UKCorrespondence: Dahai Yu, Email d.yu@keele.ac.ukPurpose: To compare estimates of annual person-consulting incidence and prevalence of low back pain (LBP) and osteoarthritis for two national English electronic health record databases (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD).Patients and Methods: Retrospective, population-based, longitudinal cohort study. LBP and osteoarthritis cases were defined using established codelists in people aged ≥ 15 and ≥ 45 years, respectively. Incident cases were new recorded cases in a given calendar year with no relevant consultation in the previous 3 years (denominator = exact person-time in the same calendar year for the at-risk population). Prevalent cases were individuals with ≥ 1 consultation for the condition of interest recorded in a given calendar year, irrespective of prior consultations for the same condition (denominator = all patients with complete registration history in the previous 3 years). We estimated age-sex standardised incidence and annual (12-month period) prevalence for both conditions in 2000– 2019, overall, and by sex, age group, and region.Results: Standardised incidence and prevalence of LBP from Aurum were lower than those from GOLD until 2014, after which estimates were similar. Both databases showed recent declines in incidence and prevalence of LBP: declines began earlier in GOLD (after 2012– 2014) than Aurum (after 2014– 2015). Standardised incidence (after 2011) and prevalence of osteoarthritis (after 2003) were higher in Aurum than GOLD and showed different trends: incidence and prevalence were stable or increasing in Aurum, decreasing in GOLD. Stratified estimates in CPRD Aurum suggested consistently higher occurrence among women, older age groups, and those living in the north of England.Conclusion: Comparative analyses of two English databases produced conflicting estimates and trends for two common musculoskeletal conditions. Aurum estimates appeared more consistent with external sources and may be useful for monitoring population musculoskeletal health and healthcare demand, but they remain sensitive to analytic decisions and data quality.Keywords: musculoskeletal, electronic health records, epidemiologic methods, osteoarthritis, low back pain, primary care

During the postpartum period, women experience neurobiological and psychosocial variations that impact language functioning. Word production in verbal fluency tasks (VFTs) is a cognitive indicator of associative (semantic categorization and phonological analysis) and executive (inhibitory control and cognitive flexibility) processes. Also, a linguistic analysis allows for understanding production strategies (e.g., orthographic and use of rhymes), with multivariate statistics facilitating cluster identification of the most common words. Considering these approaches, this study aimed to optimize semantic and phonological VFT analysis for the identification of postpartum women’s mental lexicon using quantitative and qualitative scores. These outcomes were evaluated together with sociodemographic and reproductive data of 100 postpartum women (from Argentina). Mental lexicon description was statistically improved and showed that multiparous women clustered words more concisely than primiparous women, with increased correct words and better organizational strategies. In sum, female reproductive history improved VFT outcomes. The current results also show that factor analysis can optimize the neuropsychological study of language structuring.

Background: A large number of rectal cancer patients are cured after treatment. In such cases, cure models are used for survival analysis. This study aims to investigate factors that affect survival in rectal cancer using the Cox mixture cure model. Methods: Following a retrospective design, medical documents and pathological findings of newly diagnosed rectal cancer cases hospitalized at Imam Hossein Hospital, Tehran, Iran, from 2005 to 2013 were reviewed. The patients were followed up with until May 2018. The Cox mixture cure model was used. Data analysis was carried out using Statistical Analysis System (SAS) version 9.4. The statistical significance level was considered to be 0.05. Results: Four hundred nine patients were included in this study. The mean of disease-free survival was 87.08 ± 3.2 months. The hazard of the event for the patients who were drug abusers was 2.37 (95% CI: 1.30–4.31) times more than the other cases (P = 0.005). The odds ratio of the event for patients of stage III was 3.04 (95% CI: 1.51–6.12) times more than the cases of stage I (P = 0.002), and for the patients of stage IV, it was 12.42 (95% CI: 4.17–37.01) times more than patients of stage I (P < 0.001). Conclusions: The results of this cure model indicate that the tumor stage, tumor grade, and history of drug abuse are the risk factors for the survival of patients with rectal cancer. These results can attract the attention of doctors and patients who want to be aware of their physical status and prognosis.

BackgroundSelf-harm is a leading cause of morbidity and mortality. Exposure to community violence is an important and potentially modifiable feature of the social environment that may affect self-harm, but studies to date are limited in the samples and outcomes examined.MethodsWe conducted a population-based, nested case-control study. Cases were all deaths and hospital visits due to self-harm in California, 2006-2013. We frequency-matched California resident population-based controls from the American Community Survey to cases on age, gender, race/ethnicity, and year of survey/injury. We assessed past-year community violence using deaths and hospital visits due to interpersonal violence in the community of residence. We estimated risk difference parameters that were defined to avoid extrapolation and to capture associations between changes in the distribution of community violence and the population-level risk of self-harm.ResultsAfter adjustment for confounders, setting past-year community violence to the lowest monthly levels observed within each community over the study period was associated with a 30.1 (95% confidence interval = 29.6, 30.5) per 100,000 persons per year lower risk of nonfatal self-harm but no difference in the risk of fatal self-harm. Associations for a parameter corresponding to a hypothetical violence prevention intervention targeting high-violence communities indicated a 5% decrease in nonfatal self-harm at the population level. In sensitivity analyses, results were robust.ConclusionsThis study strengthens evidence on the relationship between community violence and self-harm. Future research should investigate reasons for differential associations by age and gender and whether community violence prevention programs have meaningful impacts on self-harm.

Cause-specific mortality is an important outcome in studies of interventions to improve survival, yet causes of death can be misclassified. Here, we present an approach to performing sensitivity analyses for misclassification of cause of death in the parametric g-formula. The g-formula is a useful method to estimate effects of interventions in epidemiologic research because it appropriately accounts for time-varying confounding affected by prior treatment and can estimate risk under dynamic treatment plans. We illustrate our approach using an example comparing acquired immune deficiency syndrome (AIDS)-related mortality under immediate and delayed treatment strategies in a cohort of therapy-naive adults entering care for human immunodeficiency virus infection in the United States. In the standard g-formula approach, 10-year risk of AIDS-related mortality under delayed treatment was 1.73 (95% CI: 1.17, 2.54) times the risk under immediate treatment. In a sensitivity analysis assuming that AIDS-related death was measured with sensitivity of 95% and specificity of 90%, the 10-year risk ratio comparing AIDS-related mortality between treatment plans was 1.89 (95% CI: 1.13, 3.14). When sensitivity and specificity are unknown, this approach can be used to estimate the effects of dynamic treatment plans under a range of plausible values of sensitivity and specificity of the recorded event type.

Percutaneous coronary intervention (PCI) remains the mainstay management of symptomatic obstructive stable coronary artery disease (despite optimal medical treatment) and acute coronary syndrome. Intravascular ultrasound (IVUS) has emerged as an adjunct to angiography, permitting better assessment of the coronary lesion and stent apposition. Data from multiple studies have demonstrated improved clinical and procedural outcomes with IVUS-guided PCI. This review discusses the use of IVUS, with emphasis on technique, parameters, and applications during coronary interventions. In addition, the clinical outcomes data are highlighted with IVUS compared with conventional angiography-guided PCI.

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.

One important assumption in case-control studies is that control selection should be independent of exposure. Nevertheless, it has been hypothesized that virus interference might lead to a correlation between receipt of influenza vaccination and increased risk of infection with other respiratory viruses. We investigated whether such a phenomenon might affect a study design commonly used to estimate influenza vaccine effectiveness (VE). We searched publications in MEDLINE, PubMed, and Web of Science. We identified 12 studies using the test-negative design (2011-2017) that reported VE estimates separately derived by 3 alternative control groups: 1) all patients testing negative for influenza (FLU), VEFLU-; 2) patients who tested positive for other/another respiratory virus (ORV), VEORV+; and 3) patients who tested negative for all viruses in the panel (PAN), VEPAN-. These included VE estimates from 7 countries for all age groups from 2003/2004 to 2013/2014. We observed no difference in vaccination coverage between the ORV-positive and PAN-negative control groups. A total of 63 VEFLU- estimates, 62 VEORV+ estimates, and 33 VEPAN- estimates were extracted. Pooled estimates of the difference in VE (ΔVE) were very similar between groups. In meta-regression, no association was found between the selection of control group and VE estimates. In conclusion, we did not find any differences in VE estimates based on the choice of control group.

Influenza viruses are associated with a substantial global burden of morbidity and mortality every year. Estimates of influenza-associated mortality often vary between studies due to differences in study settings, methods, and measurement of outcomes. We reviewed 103 published articles assessing population-based influenza-associated mortality through searches of PubMed and Embase, and we identified considerable variation in the statistical methods used across studies. Studies using regression models with an influenza activity proxy applied 4 approaches to estimate influenza-associated mortality. The estimates increased with age and ranged widely, from -0.3-1.3 and 0.6-8.3 respiratory deaths per 100,000 population for children and adults, respectively, to 4-119 respiratory deaths per 100,000 population for older adults. Meta-regression analysis identified that study design features were associated with the observed variation in estimates. The estimates increased with broader cause-of-death classification and were higher for older adults than for children. The multiplier methods tended to produce lower estimates, while Serfling-type models were associated with higher estimates than other methods. No average estimate of excess mortality could reliably be made due to the substantial variability of the estimates, partially attributable to methodological differences in the studies. Standardization of methodology in estimation of influenza-associated mortality would permit improved comparisons in the future.

BackgroundCritical examination of the quality and validity of available allergic rhinitis (AR) literature is necessary to improve understanding and to appropriately translate this knowledge to clinical care of the AR patient. To evaluate the existing AR literature, international multidisciplinary experts with an interest in AR have produced the International Consensus statement on Allergy and Rhinology: Allergic Rhinitis (ICAR:AR).MethodsUsing previously described methodology, specific topics were developed relating to AR. Each topic was assigned a literature review, evidence-based review (EBR), or evidence-based review with recommendations (EBRR) format as dictated by available evidence and purpose within the ICAR:AR document. Following iterative reviews of each topic, the ICAR:AR document was synthesized and reviewed by all authors for consensus.ResultsThe ICAR:AR document addresses over 100 individual topics related to AR, including diagnosis, pathophysiology, epidemiology, disease burden, risk factors for the development of AR, allergy testing modalities, treatment, and other conditions/comorbidities associated with AR.ConclusionThis critical review of the AR literature has identified several strengths; providers can be confident that treatment decisions are supported by rigorous studies. However, there are also substantial gaps in the AR literature. These knowledge gaps should be viewed as opportunities for improvement, as often the things that we teach and the medicine that we practice are not based on the best quality evidence. This document aims to highlight the strengths and weaknesses of the AR literature to identify areas for future AR research and improved understanding.

Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these 'spillover effects' (i.e. 'herd effects') are present in the same direction as the effects on the intended recipients, studies that only estimate direct effects on recipients will likely underestimate the full public health benefits of the intervention. Causal inference assumptions for spillover parameters have been articulated in the vaccine literature, but many studies measuring spillovers of other types of public health interventions have not drawn upon that literature. In conjunction with a systematic review we conducted of spillovers of public health interventions delivered in low- and middle-income countries, we classified the most widely used spillover parameters reported in the empirical literature into a standard notation. General classes of spillover parameters include: cluster-level spillovers; spillovers conditional on treatment or outcome density, distance or the number of treated social network links; and vaccine efficacy parameters related to spillovers. We draw on high quality empirical examples to illustrate each of these parameters. We describe study designs to estimate spillovers and assumptions required to make causal inferences about spillovers. We aim to advance and encourage methods for spillover estimation and reporting by standardizing spillover parameter nomenclature and articulating the causal inference assumptions required to estimate spillovers.

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

Epidemiology has traditionally used quantitative approaches to characterizing disease prevalence and studying the effects of medical and public health interventions. Despite the power of such methods, they leave important gaps in understanding population health that can be addressed using qualitative and mixed methods. In this commentary, I describe philosophical differences in qualitative and quantitative approaches to research and explain how they can be used together to strengthen epidemiologic inquiry. [ABSTRACT FROM AUTHOR]

Ensuring that patients with opioid use disorder (OUD) have access to optimal medication therapies is a critical challenge in substance use epidemiology. Rudolph et al. (Am J Epidemiol. 2023;XXX(X):XXXX-XXXX) demonstrated that sophisticated data-adaptive statistical techniques can be used to learn optimal, individualized treatment rules that can aid providers in choosing a medication treatment modality for a particular patient with OUD. This important work also highlights the effects of the mathematization of epidemiologic research. Here, we define mathematization and demonstrate how it operates in the context of effectiveness research on medications for OUD using the paper by Rudolph et al. as a springboard. In particular, we address the normative dimension of mathematization and how it tends to resolve a fundamental tension in epidemiologic practice between technical sophistication and public health considerations in favor of more technical solutions. The process of mathematization is a fundamental part of epidemiology; we argue not for eliminating it but for balancing mathematization and technical demands equally with practical and community-centric public health needs. [ABSTRACT FROM AUTHOR]

Longitudinal data sources offer new opportunities for the evaluation of sequential interventions. To adjust for time-dependent confounding in these settings, longitudinal targeted maximum likelihood based estimation (TMLE), a doubly robust method that can be coupled with machine learning, has been proposed. This paper provides a tutorial in applying longitudinal TMLE, in contrast to inverse probability of treatment weighting and g-computation based on iterative conditional expectations. We apply these methods to estimate the causal effect of nutritional interventions on clinical outcomes among critically ill children in a United Kingdom study (Control of Hyperglycemia in Paediatric Intensive Care, 2008-2011). We estimate the probability of a child's being discharged alive from the pediatric intensive care unit by a given day, under a range of static and dynamic feeding regimes. We find that before adjustment, patients who follow the static regime "never feed" are discharged by the end of the fifth day with a probability of 0.88 (95% confidence interval: 0.87, 0.90), while for the patients who follow the regime "feed from day 3," the probability of discharge is 0.64 (95% confidence interval: 0.62, 0.66). After adjustment for time-dependent confounding, most of this difference disappears, and the statistical methods produce similar results. TMLE offers a flexible estimation approach; hence, we provide practical guidance on implementation to encourage its wider use.

Background: A large number of rectal cancer patients are cured after treatment. In such cases, cure models are used for survival analysis. This study aims to investigate factors that affect survival in rectal cancer using the Cox mixture cure model. Methods: Following a retrospective design, medical documents and pathological findings of newly diagnosed rectal cancer cases hospitalized at Imam Hossein Hospital, Tehran, Iran, from 2005 to 2013 were reviewed. The patients were followed up with until May 2018. The Cox mixture cure model was used. Data analysis was carried out using Statistical Analysis System (SAS) version 9.4. The statistical significance level was considered to be 0.05. Results: Four hundred nine patients were included in this study. The mean of disease-free survival was 87.08 ± 3.2 months. The hazard of the event for the patients who were drug abusers was 2.37 (95% CI: 1.30–4.31) times more than the other cases (P = 0.005). The odds ratio of the event for patients of stage III was 3.04 (95% CI: 1.51–6.12) times more than the cases of stage I (P = 0.002), and for the patients of stage IV, it was 12.42 (95% CI: 4.17–37.01) times more than patients of stage I (P < 0.001). Conclusions: The results of this cure model indicate that the tumor stage, tumor grade, and history of drug abuse are the risk factors for the survival of patients with rectal cancer. These results can attract the attention of doctors and patients who want to be aware of their physical status and prognosis. [ABSTRACT FROM AUTHOR]

There is growing acknowledgement of the legacy of White supremacy and racism in the discipline of epidemiology. Our department in the University of Washington School of Public Health undertook a systematic effort to begin addressing institutionalized racism and inclusive teaching in our courses. In July 2020, we introduced a new tool (the "Course Development Plan" (CDP)) to advance our curriculum. The CDP includes 2 components: 1) a guideline document that provides strategies on how to modify curricula and classroom teaching to incorporate antiracism and principles of equity, diversity, and inclusion (EDI); and 2) a structured worksheet for instructors to share EDI and antiracism practices they already incorporate and practices they plan to incorporate into their classes. Worksheets for each class are submitted prior to the beginning of the quarter and are reviewed by a peer faculty member and at least 1 epidemiology student; reviewers provide written feedback on the CDP worksheet. Further evaluation to assess the impact of the CDP process on classroom climate is ongoing. In this commentary, we discuss our department's efforts, the challenges we faced, and our hopes for next steps. [ABSTRACT FROM AUTHOR]

Measurement error is pervasive in epidemiologic research. Epidemiologists often assume that mismeasurement of study variables is nondifferential with respect to other analytical variables and then rely on the heuristic that "nondifferential misclassification will bias estimates towards the null." However, there are many exceptions to the heuristic for which bias towards the null cannot be assumed. In this paper, we compile and characterize 7 exceptions to this rule and encourage analysts to take a more critical and nuanced approach to evaluating and discussing bias from nondifferential mismeasurement. [ABSTRACT FROM AUTHOR]

Background: Malaria is transmitted by the bite of mosquitoes which belong to the genus Anopheles. The Tumbes region, located in the northern border of Peru, despite the permanent presence of the vector, was free from malaria transmission, and there are only sporadic imported cases. Material and Methods: A case series study of an outbreak of malaria caused by P. vivax associated with a conglomerate of imported cases was carried out in the context of massive Venezuelan migration to Peru. Results: The control activities implemented through the modified epidemiological fence methodology to control the reintroduction of the disease are described; transmission of the outbreak related to Venezuelan migrants in transit was detected. Conclusions: the reintroduction of malaria in the Tumbes region was related to cases imported from Venezuela. The modified epidemiological fences made it possible to improve the collection of cases in situ. [ABSTRACT FROM AUTHOR]

Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g. mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies. [ABSTRACT FROM AUTHOR]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives. [ABSTRACT FROM AUTHOR]

This article offers a comprehensive and user-friendly guide to visualizing causal theories using Single World Intervention Graphs (SWIGs). We begin with a discussion of the potential outcomes approach to causality and limitations of using Directed Acyclic Graphs (DAGs) under this framework. We then introduce SWIGs as a simple but powerful tool for integrating potential outcomes explicitly into causal diagrams. The article provides a step-by-step guide on transforming DAGs into SWIGs that includes practical insights into constructing SWIGs under various scenarios such as confounding, mediation, and sequential randomization. Highlighting the utility of SWIGs in practice, we illustrate their application in identifying the g-formula, showcasing their capacity to make causal estimands visually explicit. This article serves as a resource for epidemiologists and researchers interested in expanding their causal inference toolkit., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

How do we construct our causal directed acyclic graphs (DAGs)-for example, for life-course modeling and analysis? In this commentary, I review how the data-driven construction of causal DAGs (causal discovery) has evolved, what promises it holds, and what limitations or caveats must be considered. I find that expert- or theory-driven model-building might benefit from some more checking against the data and that causal discovery could bring new ideas to old theories., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In evidence-based medicine frameworks, the highest level of evidence is derived from quantitative synthesis of double-masked, high-quality, randomly assigned controlled trials. Meta-analyses of randomly assigned controlled trials have demonstrated that screening mammography reduces breast cancer deaths. In the United States, every major guideline-producing organization has recommended screening mammography in average-risk women; however, there are controversies about age and frequency. Carefully controlled observational research studies and statistical modeling studies can address evidence gaps and inform evidence-based, contemporary screening practices. As breast imaging radiologists develop and evaluate existing and new screening tests and technologies, they will need to understand the key methodological considerations and scientific criteria used by policy makers and health service researchers to support dissemination and implementation of evidence-based screening tests. The Wilson and Jungner principles and the U.S. Preventive Services Task Force general analytic framework provide structured evaluations of the effectiveness of screening tests. Key considerations in both frameworks include public health significance, natural history of disease, cost-effectiveness, and characteristics of screening tests and treatments. Rigorous evaluation of screening tests using analytic frameworks can maximize the benefits of screening tests while reducing potential harms. The purpose of this article is to review key methodological considerations and analytic frameworks used to evaluate screening studies and develop evidence-based recommendations., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

We conducted retrospective public health surveillance using data from 2006 to 2016 in seven integrated delivery systems from FDA's Sentinel System. We identified pediatric hypertensive patients by clinical and claims-based definitions and compared demographics, baseline profiles and follow-up time profiles. Among 3,757,803 pediatric patients aged 3 to 17 years, we identified 781,722 children and 551,246 teens with at least three blood pressure measures over 36-months. Of these, 70,315 children (9%) and 47,928 teens (8.7%) met the clinical definition for hypertension and 22,465 (2.8%) children and 60,952 (11%) of teens met the clinical definition for elevated, non-hypertensive blood pressure. Of the 3.7M patients, we identified 3,246 children and 7,293 teens with any claim for hypertension (claims definition). Evidence of hypertension claims among those meeting our clinical definition was poor; 2.2% and 7.3% of clinically hypertensive children and teens had corresponding claims for hypertension. Baseline profiles for claims-based hypertensive patients suggest greater severity of disease compared to clinical patients. Claims-based patients showed higher rates of all-cause mortality during follow-up. Pediatric hypertension in claims-based data sources is under-captured but may serve as a marker for greater disease severity. Investigators should understand coding practices when selecting real-world data sources for future pediatric hypertension work., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: The Spectrum/AIM model is used by national HIV programs and UNAIDS to prepare annual estimates of key HIV indicators. This article describes key updates to paediatric and adult models for the 2021 round of HIV estimates. Methods: Potential updates to Spectrum arise due to newly available data, new analyses of existing data, and the need for new issues to be addressed. Updates are guided by experts through the UNAIDS Reference Group on Estimates, Modelling and Projections. Changes are tested and assessed for impact before being accepted into the final model. Results: Spectrum tracks children living with HIV by CD4% for ages 0-4 and CD4 count for ages 5-14. Data from IeDEA treatment sites have been used to map the transition from CD4% to CD4 count at age 5. Breastfeeding patterns in sub-Saharan Africa have been updated with the latest survey data and estimates of continuation on antiretroviral therapy (ART) with breastfeeding have been revised based on recent studies. Model assumptions about the CD4 counts of people who drop out of ART have been revised to account for CD4 count increases while on treatment. If available, monthly data on numbers on ART can now be used to estimate the effects of COVID-19-related disruptions during 2020. Conclusions: These changes are intended to provide more accurate estimates of HIV burden. The effects of these changes on paediatric indicators are small except in countries with new surveys that might have updated patterns of breastfeeding. Changes to the adult model have little effect on total new infections. AIDS-related deaths will be somewhat lower in countries that have data on ART drop out but might be increased by HIV care disruptions due to COVID-19. The updated model uses newly available data to improve the estimation of paediatric and adult HIV indicators. Keywords: CD4; COVID-19; estimates; HIV; mortality; Spectrum, 1 | INTRODUCTION Most countries prepare annual estimates of key HIV indicators which are used for national planning and published annually by UNAIDS [1]. UNAIDS provides technical assistance through the [...]

Research relating to machine learning algorithms, including convolutional neural networks, has increased during the past 5 years. The aim of this pilot study was to investigate how accurately a convolutional neural network, trained on Swedish registry data, could perform in predicting cutaneous invasive and in situ melanoma (CMM) within 5 years. A cohort of 1,208,393 individuals was used. Registry data ranged from 4 July 2005 to 31 December 2011, predicting CMM between 1 January 2012 and 31 December 2016. A convolutional neural network with one-dimensional convolutions with respect to time was trained using healthcare databases and registers. The algorithm was trained on 23,886 individuals. Validation was performed on a holdout validation set including 6,000 individuals. After training and validation, the convolutional neural network was evaluated on a test set (1,000 individuals with an CMM occurring within 5 years and 5,000 without). The area under the receiver-operating characteristic curve was 0.59 (95% confidence interval (95% CI) 0.57–0.61). The point on the receiver-operating characteristic curve where sensitivity equalled specificity had a value of 56% (sensitivity 95% CI 53–60% and specificity 95% CI 55–58%). Albeit at an early stage, this pilot investigation demonstrates potential usefulness for machine learning algorithms in predicting melanoma risk.

Purpose: Passively generated cell-phone location ("mobility") data originally intended for commercial use has become frequently used in epidemiologic research, notably during the COVID-19 pandemic to study the impact of physical-distancing recommendations on aggregate population behavior (e.g., average daily mobility). Given the opaque nature of how individuals are selected into these datasets, researchers have cautioned that their use may give rise to selection bias, yet little guidance exists for assessing this potential threat to validity in mobility-data research. Through an example analysis of cell-phone-derived mobility data, we present a set of conditions to guide the assessment of selection bias in measures comparing aggregate mobility patterns over time and between groups.Methods: We specifically consider bias in measures comparing group-level mobility in the same group (difference, ratio, percent difference) and between groups (difference in differences, ratio of ratios, ratio of percent differences). We illustrate no-bias conditions in these measures through an example comparing block-group-level mobility between income groups in United States metro areas before (January 1st-March 10, 2020) and after (March 11th-April 19th, 2020) the day COVID-19 was declared a pandemic.Results: Within-group contrasts describing mobility over time, especially for the higher-income decile, were expected to be most resistant to bias during the example study period.Conclusions: The presented conditions can be used to assess the susceptibility to selection bias of group-level measures comparing mobility. Importantly, they can be used even without knowledge of the degree of bias in each group at each time point. We further highlight links between no-bias principles originating in epidemiology and economics, showing that certain assumptions (e.g., parallel trends) can apply to biases beyond their original application. [ABSTRACT FROM AUTHOR]

Objective: This study evaluated the relationships between the occurrence of recent and recurring natural disasters on the incidence of acute and chronic health outcomes at the census tract level in 500 cities across the United States between 2001 and 2015. Methods: Using the Centers for Disease Control and Prevention (CDC) 500 cities data set, the CDC Social Vulnerability Index, and the US Small Business Administration (SBA) Disaster Loan Database, we modeled the incidence of self-reported, poor mental and physical health, or a clinical diagnosis of high blood pressure or asthma in census tracts (N = 27 204 tracts in 500 cities) that had experienced recent or recurring natural disasters while controlling for social and environmental risk factors. Results: Communities that experienced a natural disaster in the previous 5 years compared to those that had not had a higher incidence of poor mental health (RR: 1.02, 95% CI: 1.01-1.02), poor physical health (RR: 1.03, 95% CI: 1.02-1.04), high blood pressure (RR: 1.04, 95% CI: 1.02-1.05), and asthma (RR: 1.01, 95% CI: 1.01-1.02). The incidence of these poor health outcomes increased 1-2% with each additional year that a community experienced a disaster. Conclusions: Prevention and preparedness plans that work to build resilience in communities before disasters should focus on closing the gap in environmental and social determinants that have been linked with disproportionate health burdens and slow recovery post-disaster. [ABSTRACT FROM AUTHOR]

Observational studies of oseltamivir use and influenza complications could suffer from residual confounding. Using negative control risk periods and a negative control outcome, we examined confounding control in a health-insurance-claims–based study of oseltamivir and influenza complications (pneumonia, all-cause hospitalization, and dispensing of an antibiotic). Within the Food and Drug Administration's Sentinel System, we identified individuals aged ≥18 years who initiated oseltamivir use on the influenza diagnosis date versus those who did not, during 3 influenza seasons (2014–2017). We evaluated primary outcomes within the following 1–30 days (the primary risk period) and 61–90 days (the negative control period) and nonvertebral fractures (the negative control outcome) within days 1–30. We estimated propensity-score–matched risk ratios (RRs) per season. During the 2014–2015 influenza season, oseltamivir use was associated with a reduction in the risk of pneumonia (RR = 0.72, 95% confidence interval (CI): 0.70, 0.75) and all-cause hospitalization (RR = 0.54, 95% CI: 0.53, 0.55) in days 1–30. During days 61–90, estimates were near-null for pneumonia (RR = 1.04, 95% CI: 0.95, 1.15) and hospitalization (RR = 0.94, 95% CI: 0.91, 0.98) but slightly increased for antibiotic dispensing (RR = 1.14, 95% CI: 1.08, 1.21). The RR for fractures was near-null (RR = 1.09, 95% CI: 0.99, 1.20). Estimates for the 2016–2017 influenza season were comparable, while the 2015–2016 season had conflicting results. Our study suggests minimal residual confounding for specific outcomes, but results differed by season. [ABSTRACT FROM AUTHOR]

Various index systems and indicators have been used to assess periodontal status in epidemiological studies. The United States National Health and Nutrition Examination Survey, 2009‒2014, use a standard for periodontal measurements, including probing at six tooth sites, with an assessment of the clinical attachment loss and probing pocket depth in all teeth in dentition except third molars, to identify a case and severity of periodontitis. This standard significantly improves the validity of the assessment. According to a recent consensus, the epidemiologic hallmark of periodontitis are the clinical attachment loss and radiographic bone loss. The data on the prevalence and progression of periodontitis report significant differences in geographical area. Numerous epidemiological studies have identified risk factors for periodontitis, including age, male gender, minority groups, socioeconomic factors, smokers, and uncontrolled diabetes. The reproduction of standard surveillance protocols would allow taking into account the trends in the prevalence and progression of periodontitis over time and by geographical areas, on which basis measures can be taken for their reduction. [ABSTRACT FROM AUTHOR]

Racial residential segregation is associated with multiple adverse health outcomes in Black individuals. Yet, the influence of structural racism and racial residential segregation on brain aging is less understood. In this study, we investigated the association between cumulative exposure to racial residential segregation over 25 years (1985–2010) in young adulthood, as measured by the Getis-Ord Gi* statistic, and year 25 measures of brain volume (cerebral, gray matter, white matter, and hippocampal volumes) in midlife. We studied 290 Black participants with available brain imaging data who were enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort study. CARDIA investigators originally recruited 2,637 Black participants aged 18–30 years from 4 field centers across the United States. We conducted analyses using marginal structural models, incorporating inverse probability of treatment weighting and inverse probability of censoring weighting. We found that compared with low/medium segregation, greater cumulative exposure to a high level of racial residential segregation throughout young adulthood was associated with smaller brain volumes in general (e.g. for cerebral volume, β = –0.08, 95% confidence interval: −0.15, −0.02) and with a more pronounced reduction in hippocampal volume, though results were not statistically significant. Our findings suggest that exposure to segregated neighborhoods may be associated with worse brain aging. [ABSTRACT FROM AUTHOR]

In their recent article, Ogburn et al. (Am J Epidemiol. 2021;190(6):1142–1147) raised a cautionary tale for epidemiologic data fusion: Bias may occur if a variable that is completely missing in the primary data set is imputed according to a regression model estimated from an auxiliary data set. However, in some specific settings, a solution may exist. Focusing on a linear outcome regression model with a missing covariate, we show that the bias can be eliminated if the underlying imputation model for the missing covariate is nonlinear in the common variables measured in both data sets. Otherwise, we describe 2 alternative approaches existing in the data fusion literature that could partially resolve this issue: One fits the outcome model by leveraging an additional validation data set containing joint observations of the outcome and the missing covariate, and the other offers informative bounds for the outcome regression coefficients without using validation data. We justify these 3 methods in a linear outcome model and briefly discuss their extension to general settings. [ABSTRACT FROM AUTHOR]

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Objective: There is a paucity of scientific analysis that has examined spatial heterogeneities in the socioeconomic vulnerabilities related to coronavirus disease 2019 (COVID-19) risk and potential mitigation strategies at the sub-national level in India. The present study examined the demographic, socioeconomic, and health system-related vulnerabilities shaping COVID-19 risk across 36 states and union territories in India. Methods: Using secondary data from the Ministry of Health and Family Welfare (MoHFW), Government of India; Census of India, 2011; National Family Health Survey, 2015-16; and various rounds of the National Sample Survey, we examined socioeconomic vulnerabilities associated with COVID-19 risk at the sub-national level in India from March 16, 2020, to May 3, 2020. Descriptive statistics, principal component analysis, and the negative binomial regression model were used to examine the predictors of COVID-19 risk in India. Results: There persist substantial heterogeneities in the COVID-19 risk across states and union territories in India. The underlying demographic, socioeconomic, and health infrastructure characteristics drive the vulnerabilities related to COVID-19 in India. Conclusions: This study emphasizes that concerted socially inclusive policy action and sustained livelihood/economic support for the most vulnerable population groups is critical to mitigate the impact of the COVID-19 pandemic in India. [ABSTRACT FROM AUTHOR]

Abstract Background Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012. Methods A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012. Results Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls. Conclusion The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

Abstract Background The continuum of resistance model’s premise is that delayed respondents to a survey are more similar to non-respondents than early respondents are. For decades, survey researchers have applied this model in attempts to evaluate and adjust for non-response bias. Despite a recent resurgence in the model’s popularity, its value has only been assessed in one large online population health survey. Methods Respondents to the Norwegian Counties Public Health Survey in Hordaland, Norway, were divided into three groups: those who responded within 7 days of the initial email/SMS invitation (wave 1, n = 6950); those who responded after 8 to 14 days and 1 reminder (wave 2, n = 4950); and those who responded after 15 or more days and 2 reminders (wave 3, n = 4045). Logistic regression analyses were used to compare respondents’ age, sex and educational level between waves, as well as the prevalence of poor general health, life dissatisfaction, mental distress, chronic health problems, weekly alcohol consumption, monthly binge drinking, daily smoking, physical activity, low social support and receipt of a disability pension. Results The overall response to the survey was 41.5%. Respondents in wave 1 were more likely to be older, female and more highly educated than those in waves 2 and 3. However, there were no substantial differences between waves for any health outcomes, with a maximal prevalence difference of 2.6% for weekly alcohol consumption (wave 1: 21.3%, wave 3: 18.7%). Conclusions There appeared to be a mild continuum of resistance for demographic variables. However, this was not reflected in health and related outcomes, which were uniformly similar across waves. The continuum of resistance model is unlikely to be useful to adjust for nonresponse bias in large online surveys of population health.

AimsTo appraise the clinical and genetic evidence that low-density lipoproteins (LDLs) cause atherosclerotic cardiovascular disease (ASCVD).Methods and resultsWe assessed whether the association between LDL and ASCVD fulfils the criteria for causality by evaluating the totality of evidence from genetic studies, prospective epidemiologic cohort studies, Mendelian randomization studies, and randomized trials of LDL-lowering therapies. In clinical studies, plasma LDL burden is usually estimated by determination of plasma LDL cholesterol level (LDL-C). Rare genetic mutations that cause reduced LDL receptor function lead to markedly higher LDL-C and a dose-dependent increase in the risk of ASCVD, whereas rare variants leading to lower LDL-C are associated with a correspondingly lower risk of ASCVD. Separate meta-analyses of over 200 prospective cohort studies, Mendelian randomization studies, and randomized trials including more than 2 million participants with over 20 million person-years of follow-up and over 150 000 cardiovascular events demonstrate a remarkably consistent dose-dependent log-linear association between the absolute magnitude of exposure of the vasculature to LDL-C and the risk of ASCVD; and this effect appears to increase with increasing duration of exposure to LDL-C. Both the naturally randomized genetic studies and the randomized intervention trials consistently demonstrate that any mechanism of lowering plasma LDL particle concentration should reduce the risk of ASCVD events proportional to the absolute reduction in LDL-C and the cumulative duration of exposure to lower LDL-C, provided that the achieved reduction in LDL-C is concordant with the reduction in LDL particle number and that there are no competing deleterious off-target effects.ConclusionConsistent evidence from numerous and multiple different types of clinical and genetic studies unequivocally establishes that LDL causes ASCVD.