Search

With advances in natural language processing and machine learning, researchers are leveraging social media as a low-cost, low-burden method for measuring various psychosocial factors. However, it is unclear whether information derived from social media is generalizable to broader populations, especially middle-aged and older adults. Using data on women aged 53–70 years from Nurses' Health Study II (2017–2018; n  = 49,045), we assessed differences in sociodemographic characteristics, health conditions, behaviors, and psychosocial factors between regular and nonregular users of Facebook (Facebook, Inc., Menlo Park, California). We evaluated effect sizes with phi (φ) coefficients (categorical data) or Cohen's d (continuous data) and calculated odds ratios with 95% confidence intervals. While most comparisons between regular and nonregular users achieved statistical significance in this large sample, effect sizes were mostly "very small" (conventionally defined as φ or d <0.01) (e.g., optimism score: meanregular users = 19 vs. meannonregular users = 19 (d  = −0.03); physical activity: meanregular users = 24 metabolic equivalent of task (MET)-hours/week vs. meannonregular users = 24 MET-hours/week (d =  0.01)). Some factors had slightly larger differences for regular users versus nonregular users (e.g., depression: 28% vs. 23% (φ = 0.05); odds ratio = 1.27 (95% confidence interval: 1.22, 1.33); obesity: 34% vs. 26% (φ = 0.07); odds ratio = 1.42 (95% confidence interval: 1.36, 1.48)). Results suggest that regular Facebook users were similar to nonregular users across sociodemographic and psychosocial factors, with modestly worse health regarding obesity and depressive symptoms. In future research, investigators should evaluate other demographic groups. [ABSTRACT FROM AUTHOR]

Heterogeneous exposure associations (HEAs) can be defined as differences in the association of an exposure with an outcome among subgroups that differ by a set of characteristics. In this article, we intend to foster discussion of HEAs in the epidemiologic literature and present a variant of the random forest algorithm that can be used to identify HEAs. We demonstrate the use of this algorithm in the setting of the association between systolic blood pressure and death in older adults. The training set included pooled data from the baseline examination of the Cardiovascular Health Study (1989–1993), the Health, Aging, and Body Composition Study (1997–1998), and the Sacramento Area Latino Study on Aging (1998–1999). The test set included data from the National Health and Nutrition Examination Survey (1999–2002). The hazard ratios ranged from 1.25 (95% confidence interval: 1.13, 1.37) per 10-mm Hg increase in systolic blood pressure among men aged ≤67 years with diastolic blood pressure greater than 80 mm Hg to 1.00 (95% confidence interval: 0.96, 1.03) among women with creatinine concentration ≤0.7 mg/dL and a history of hypertension. HEAs have the potential to improve our understanding of disease mechanisms in diverse populations and guide the design of randomized controlled trials to control exposures in heterogeneous populations. [ABSTRACT FROM AUTHOR]

Interpreting the results of epidemiologic studies calls for objectivity and rigorous scrutiny, acknowledging the limitations that temper the applicability of the findings to public health action. Current trends have posed new challenges to balancing goals of scientific objectivity and validity with public health applications. The ongoing tension between epidemiology's aspirations and capability has several sources: the need to overpromise in research proposals, compromising methodological rigor because of public health importance, defending findings in the face of hostile critics, and appealing to core constituencies who have specific expectations from the research. [ABSTRACT FROM AUTHOR]

Interpreting the results of epidemiologic studies calls for objectivity and rigorous scrutiny, acknowledging the limitations that temper the applicability of the findings to public health action. Current trends have posed new challenges to balancing goals of scientific objectivity and validity with public health applications. The ongoing tension between epidemiology's aspirations and capability has several sources: the need to overpromise in research proposals, compromising methodological rigor because of public health importance, defending findings in the face of hostile critics, and appealing to core constituencies who have specific expectations from the research. [ABSTRACT FROM AUTHOR]

Background: Fifteen regional studies published over the last six decades surveying prevalence, mortality and hospital admissions have suggested that Scotland is amongst the highest risk nations for multiple sclerosis (MS) in the world. However, substantial intranational variation in rates (between regions) has been described in numerous countries, including in the only previous Scottish national survey, which used hospital admission data, to address this issue. Against this backdrop, the Scottish Multiple Sclerosis Register (SMSR) was established in 2010 to prospectively collect nationally comprehensive incidence data and to allow for regional comparisons. Methods: Here, we present the SMSR and analyse the variation in crude and age–sex standardized incidence rates, lifetime risk (cumulative incidence), and the sex distribution of cases and rates, between the 14 administrative Health Boards or regions of Scotland: 01 January 2010 to 31 December 2017. Results: The overall incidence rate for Scotland was 8.76/100,000 person-years (standardized: 8.54). Regional incidence rates varied significantly—up to threefold—between Health Boards (p < 1 × 10–13). The national female-to-male sex ratio was 2.3:1, but this too varied regionally (outlier regions result in a range from 1.0 to 4.2:1). Lifetime risk ranged from 19.9/1000 for females in Orkney (58.98°N) to 1.6/1000 for males in the Borders (55.60°N). Comparison with a previous national survey suggests that these differences are longstanding. In 6 of 14 regions the lifetime risk for women exceeds 1%. Conclusions: This study introduces a national incidence register: a valuable research tool and the result of substantial public investment. The wide variation in incidence rates and sex ratios between regions, in a relatively homogenous population, raises questions for future study. [ABSTRACT FROM AUTHOR]

Objectives Several types of epidemiologic studies suffer from decreasing participation rates, resulting in potential selection bias and delay or termination of studies. We aimed to determine the feasibility of online methods for recruitment of pregnant women into a prospective cohort study. Methods In addition to traditional recruitment through prenatal care providers, we advertized participation in the PRegnancy and Infant DEvelopment (PRIDE) Study, an ongoing prospective cohort study with long-term follow-up in The Netherlands enrolling women in early pregnancy, through Google AdWords (30 days) and Facebook Ads (31 and 27 days) campaigns between September 2016 and January 2017. We calculated costs per eligible participant and compared demographics, health-related characteristics, and follow-up rates between participants recruited through online methods and prenatal care providers. Results During the study period, we recruited six women through AdWords (€54.28 per participant), 59 through Facebook (€10.17 per participant), and 327 through prenatal care providers (no valid cost estimate available). Facebook participants seemed to be younger (29.0 vs. 30.7 years), to have a higher body mass-index and/or low/intermediate education (27.0 vs. 24.0 kg/m2 and 41 vs. 25%, respectively), and to start prenatal care in secondary care more often (12 vs. 5%) than participants recruited through prenatal care providers. Item non-response and loss to follow-up rates were higher among women recruited online than among those recruited through prenatal care providers. Conclusion Google AdWords did not contribute substantially, but Facebook Ads may complement traditional recruitment methods of pregnant women into prospective cohort studies, despite challenges that may threaten internal validity. [ABSTRACT FROM AUTHOR]

Aim: Duration of untreated psychosis (DUP) is considered as a key prognostic variable in psychosis. Yet, it is unclear whether a longer DUP causes worse outcomes or whether reported associations have alternative explanations. Methods: Data from 2 cohorts of patients with first episode psychosis were used (n = 2134). Measures of DUP were assessed at baseline and outcomes at 12 months. Regression models were used to investigate the associations between DUP and outcomes. We also investigated whether any associations were replicated using instrumental variables (IV) analysis to reduce the effect of residual confounding and measurement bias. Results: There were associations between DUP per 1‐year increase and positive psychotic symptoms (7.0% in symptom score increase 95% confidence interval (CI) 4.0%, 10.0%, P < .001), worse recovery (risk difference [RD] 0.78, 95%, CI 0.68, 0.83, P < .001) and worse global functioning (0.62 decrease in functioning score 95% CI −1.19, −0.04, P = .035). There was no evidence of an association with negative psychotic symptoms (1.0%, 95%, CI −2.0%, 5.0%, P = .455). The IV analysis showed weaker evidence of associations in the same direction between DUP per 1‐year increase and positive psychotic symptoms, recovery and global functioning. However, there was evidence of an inverse association with negative psychotic symptoms (decrease of 15.0% in symptom score 95% CI −26.0%, −3.0%, P = .016). Conclusions: We have confirmed previous findings of a positive association between positive psychotic symptoms, global functioning and recovery and DUP using regression analysis. IV analysis shows some support for these findings. Future investigation using IV analysis should be repeated in large data sets. [ABSTRACT FROM AUTHOR]

Matching methods are assumed to reduce the likelihood of a biased inference compared with ordinary least squares (OLS) regression. Using simulations, we compared inferences from propensity score matching, coarsened exact matching, and unmatched covariate-adjusted OLS regression to identify which methods, in which scenarios, produced unbiased inferences at the expected type I error rate of 5%. We simulated multiple data sets and systematically varied common support, discontinuities in the exposure and/or outcome, exposure prevalence, and analytical model misspecification. Matching inferences were often biased in comparison with OLS, particularly when common support was poor; when analysis models were correctly specified and common support was poor, the type I error rate was 1.6% for propensity score matching (statistically inefficient), 18.2% for coarsened exact matching (high), and 4.8% for OLS (expected). Our results suggest that when estimates from matching and OLS are similar (i.e. confidence intervals overlap), OLS inferences are unbiased more often than matching inferences; however, when estimates from matching and OLS are dissimilar (i.e. confidence intervals do not overlap), matching inferences are unbiased more often than OLS inferences. This empirical "rule of thumb" may help applied researchers identify situations in which OLS inferences may be unbiased as compared with matching inferences. [ABSTRACT FROM AUTHOR]

Background: The transition from normal cognition to Alzheimer's disease is considered a continuum, with amnestic mild cognitive impairment (aMCI) an intermediate clinical cognitive state. Although prior work suggests that dementia incidence rates may be declining, there is little information regarding temporal trends in aMCI incidence.Objective: To determine whether age specific rates of aMCI have changed over sequential birth cohorts among individuals included in the population-based Einstein Aging Study (EAS) cohort. A secondary objective was to examine trends in aMCI rates among Blacks and Whites and by sex.Methods: Age specific incidence of aMCI was examined by birth year among 1,233 individuals age 70 years and above enrolled in the population-based EAS cohort between November 1, 1993 and February 22, 2016 and who had at least one annual follow-up assessment (5,321 person years of follow-up). Poisson regression was used to determine whether there has been a change in age specific aMCI rates over sequential years of birth.Results: No significant change in aMCI rates was identified in the overall cohort, among Blacks or Whites, or among males or females born between 1899 and 1946.Conclusions: Despite a trend for decreased dementia incidence in the EAS cohort, rates of incident aMCI have not changed. These apparently conflicting results may indicate a delay or decrease in the rates of transition from aMCI to dementia within the cohort. However, further studies are needed to confirm whether rates of aMCI have changed in other populations, and how aMCI rates are related to secular trends in dementia risk factors. [ABSTRACT FROM AUTHOR]

The potential for immortal time bias is pervasive in epidemiologic studies with left truncation or time-varying exposures. Unlike other biases in epidemiologic research (e.g., measurement bias, confounding due to unmeasured factors, and selection based on unmeasured predictors of the outcome), immortal time bias can and should be avoided by the correct assignment of person-time during follow up. However, even when handing person-time correctly, allowing late entry into a study or into an exposure group can open the door to more insidious sources of bias, some of which we explore here. Clear articulation of the study question, including the treatment plans of interest, can provide navigation around these sources of bias and elucidate the assumptions needed for inference given the available data. Here, we use simulated data to illustrate the assumptions required under various approaches to estimate the effect of a time-varying treatment and describe how these assumptions relate to the assumptions necessary to estimate single sample rates and risks in settings with censoring and truncation. [ABSTRACT FROM AUTHOR]

There are now many published applications of causal (structural) models for estimating effects of time-varying exposures in the presence of confounding by earlier exposures and confounders affected by earlier exposures. Results from these models can be highly sensitive to inclusion of lagged and baseline exposure terms for different visits. This sensitivity is often overlooked in practice; moreover, results from these models are not directly comparable to results from conventional time-dependent regression models, because the latter do not estimate the same causal parameter even when no bias is present. We thus explore the implications of including lagged and baseline exposure terms in causal and regression models, using a public data set (Caerphilly Heart Disease Study in the United Kingdom, 1979–1998) relating smoking to cardiovascular outcomes. [ABSTRACT FROM AUTHOR]

In analytical observational studies, researchers try to establish an association between exposure(s) and outcome(s). Depending on the direction of enquiry, these studies can be directed forwards (cohort studies) or backwards (case–control studies). In this article, we examine the key features of these two types of studies. [ABSTRACT FROM AUTHOR]

Purpose: " parkrun" is a free and increasingly popular weekly 5-km walk/run international community event, representing a novel setting for physical activity (PA) promotion. However, little is known about who participates or why. This study aimed to identify sociodemographic, health, behavioral, individual, social, and environmental factors associated with higher levels of participation.Design: Cross-sectional.Setting: Tasmania, Australia; June 2016.Participants: Three hundred seventy two adult parkrun participants.Measures: Online survey measuring sociodemographic, health, individual, social and environmental factors, parkrun participation, and PA.Analysis: Descriptive statistics, zero-truncated Poisson regression models.Results: Respondents (n = 371) were more commonly women (58%), aged 35 to 53 years (54%), and occasional or nonwalkers/runners (53%) at registration. A total of 44% had overweight/obesity. Half had non-adult children, most spoke English at home, and 7% reported PA-limiting illness/injury/disability. Average run/walk time was 30.2 ± 7.4 minutes. Compared to regular walkers/runners at registration, nonwalkers/runners were less commonly partnered, more commonly had overweight/obesity, less physically active, and had poorer self-rated health. Multivariate analyses revealed relative parkrun participation was inversely associated with education level and positively associated with interstate parkrun participation, perceived social benefits, self-efficacy for parkrun, and intentions to participate.Conclusion: parkrun attracts nonwalkers/runners and population groups hard to engage in physical activity. Individual- and social-level factors were associated with higher relative parkrun participation. parkrun's scalability, accessibility, and wide appeal confers a research imperative to investigate its potential for public health gain. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) is used to answer a variety of epidemiologic questions. One stated advantage of MR is that it estimates a "lifetime effect" of exposure, though this term remains vaguely defined. Instrumental variable analysis, on which MR is based, has focused on estimating the effects of point or time-fixed exposures rather than "lifetime effects." Here we use an empirical example with data from the Rotterdam Study (Rotterdam, the Netherlands, 2009–2013) to demonstrate how confusion can arise when estimating "lifetime effects." We provide one possible definition of a lifetime effect: the average change in outcome measured at time t when the entire exposure trajectory from conception to time t is shifted by 1 unit. We show that MR only estimates this type of lifetime effect under specific conditions—for example, when the effect of the genetic variants used on exposure does not change over time. Lastly, we simulate the magnitude of bias that would result in realistic scenarios that use genetic variants with effects that change over time. We recommend that investigators in future MR studies carefully consider the effect of interest and how genetic variants whose effects change with time may impact the interpretability and validity of their results. [ABSTRACT FROM AUTHOR]

The popular inverse probability weighting method in causal inference is often hampered by extreme propensity scores, resulting in biased estimates and excessive variance. A common remedy is to trim patients with extreme scores (i.e. remove them from the weighted analysis). However, such methods are often sensitive to the choice of cutoff points and discard a large proportion of the sample. The implications for bias and the precision of the treatment effect estimate are unclear. These problems are mitigated by a newly developed method, the overlap weighting method. Overlap weights emphasize the target population with the most overlap in observed characteristics between treatments, by continuously down-weighting the units in the tails of the propensity score distribution. Here we use simulations to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance, and 95% confidence interval coverage. A range of propensity score distributions are considered, including settings with substantial nonoverlap and extreme values. To facilitate practical implementation, we further provide a consistent estimator for the standard error of the treatment effect estimated using overlap weighting. [ABSTRACT FROM AUTHOR]

Introduction and Aims: There is a paucity of data on volume of alcohol use from Muslim majority countries. We aimed to present estimation methods for alcohol consumption with the use of survey data for these societies and provide an estimation for age 15+ per capita consumption of pure alcohol for Iran.Design and Methods: The Iranian Mental Health Survey was a nationally representative household survey on individuals aged 15-64 years, with a multistage, cluster sampling design. We used the 'Last Week' method and 'Quantity-Frequency' methods for gathering data on alcohol consumption and combined these to provide more complete estimates.Results: The response rate was 85.7%. From the total of 7840 respondents, 5.7% and 1% reported past 12 months and past week alcohol use, respectively. The highest estimation for age 15+ per capita consumption of pure alcohol was yielded by the 'combination method' (0.108 L ethanol/person/year) followed by the Quantity-Frequency method (0.079 L). The 'Last Week' method provided the lowest estimate (0.059 L).Discussion and Conclusions: Unlike in surveys of non-Muslim countries, frequency of drinking from recent recall (last week) was much lower than from recall of usual drinking in the last year. We conclude that 0.108 L (SE = 0.03) is the best survey-based estimate of age 15+ per capita consumption, which translates to about 5 750 000 L of national consumption per year in Iran. However, this method is still likely to under-estimate per capita consumption due to evidence of under-reporting in the survey. [ABSTRACT FROM AUTHOR]

Abstract Background Measuring vaccine effectiveness (VE) relies on the use of observational study designs. However, achieving robust estimates of direct and indirect VE is frequently compromised by bias, particularly when using syndromic diagnoses of low-specificity. Methods In order to mitigate confounding between the measured outcome and vaccine uptake, we developed a method to balance comparator populations using individual-level propensity scoring derived from the vaccine-exposed population, and applied it to the unexposed comparator population. Indirect VE was estimated by comparing the unvaccinated vaccine-exposed group with a propensity score-simulated unvaccinated, unexposed group. Direct VE was derived by removing indirect VE from the overall VE. We applied this method to an evaluation of the effectiveness of infant rotavirus vaccination in the UK. Using a general practice cohort of 45,259 live births between May 2010 and December 2015, we calculated indirect and direct VE against consultations for acute gastroenteritis using conventional and vaccination-propensity adjustment comparator populations. Results The overall VE during the rotavirus-season (January-May) calculated using mixed-effects Cox regression was 30% [95% confidence intervals (95% CI: 25,35%)]. Use of conventional comparator populations resulted in implausible VE estimates −14% (95% CI: −41,7%) for direct and 29% (95% CI: 14,42%) for indirect effects. Applying our alternative method, direct VE was 26% (95% CI: 1,45%) and indirect VE was 8% (95% CI: −19,29%). Conclusions Estimating VE using propensity score simulated comparator populations, particularly for studies using routine health data with syndromic, low-specificity endpoints will aid accurate measurement of the broader public health impact of a vaccine programme. [ABSTRACT FROM AUTHOR]

Purpose: Coefficients of determination (R2) for continuous longitudinal data are typically reported as time constant, if they are reported at all. The widely used mixed model with random intercepts and slopes yields the total outcome variance as a time-varying function. We propose a generalized and intuitive approach based on this variance function to estimate the time-varying predictive power (R2) of a variable on outcome levels and changes.Methods: Using longitudinal estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease in Children Study, linear mixed models characterized the R2 for two chronic kidney disease (CKD) risk factors measured at baseline: a traditional marker (proteinuria) and a novel marker (fibroblast growth factor 23 [FGF23]).Results: Time-varying R2 divulged different disease processes by risk factor and diagnoses. Among children with glomerular CKD, time-varying R2 for proteinuria had significant upward trends, suggesting increasing power to predict eGFR change, but crossed with FGF23, which was higher up to 2.5 years from baseline. In contrast, among those with nonglomerular CKD, proteinuria explained more than FGF23 at all times, and time-varying R2 for each risk factor was not substantially different from time-constant estimates.Conclusions: Proteinuria and FGF23 explained substantial eGFR variability over time. Time-varying R2 can characterize predictive roles of risk factors on disease progression, overcome limitations of time-constant estimates, and are easily derived from mixed effects models. [ABSTRACT FROM AUTHOR]

Background: Randomized controlled trials at times investigate findings suggested by observational studies. For example, the Towards a Revolution in COPD Health (TORCH) trial, which did not show a mortality reduction with inhaled corticosteroids (ICS) in COPD, was motivated by some observational studies that suggested considerable reductions in mortality with these drugs. Reasons for these discrepancies are unclear.Methods: The literature was searched to identify all observational studies, including cohort and case-control studies, investigating the effectiveness of ICS on major outcomes in patients with COPD; these outcomes included death and hospitalization for COPD.Results: A total of 21 studies were identified. Nine studies were affected by immortal time bias, five by immeasurable time bias, and seven by the "asthma factor" bias; some studies were affected by more than one bias. These studies found important reductions in the rates of major COPD outcomes with ICS use, with pooled rate ratios of 0.71 (95% CI, 0.67-0.76), 0.76 (95% CI, 0.70-0.83), and 0.79 (95% CI, 0.73-0.87), respectively, for the three sources of bias. In contrast, the five studies unaffected by these major biases did not find an association (pooled rate ratio, 1.02 [95% CI, 0.88-1.17]).Conclusions: Observational studies are important to provide evidence from real-world data on medication effects. However, appropriate study design and analysis are essential to avoid biases and ensure randomized trials with greater chances of success. The observational studies suggesting potential beneficial effects of nonrespiratory drugs to treat COPD, such as statins and beta-blockers, will also need careful review before long and expensive randomized trials are conducted. [ABSTRACT FROM AUTHOR]

Brazil is experiencing among the world's fastest demographic aging worldwide. This demographic transition is occurring in a context of few resources and great social inequalities. The Brazilian Longitudinal Study of Aging (ELSI-Brazil) is a nationally representative study of 9,412 people aged 50 years or older, residing in 70 municipalities across the 5 Brazilian regions. ELSI-Brazil allows investigations of the aging process, its health, psychosocial and economic determinants, and societal consequences. The baseline examination (2015-2016) included detailed household and individual interviews and physical measurements (blood pressure, anthropometry, grip strength, and timed walk and balance tests). Blood tests and sample storage were performed in a subsample of study participants. Subsequent waves are planned for every 3 years. The study adopts a conceptual framework common to other large-scale longitudinal studies of aging in the world, such as the Health and Retirement Study, allowing cross-national comparisons. The goal of ELSI-Brazil is not only to build an understanding of aging in a large, Western, middle-income country in a rapid demographic transition but also to provide scientific data to support and study policy changes that may affect older adults. We describe the methodology of the study and some descriptive results of the baseline survey. [ABSTRACT FROM AUTHOR]

Purpose: Chiropractic care is popular for low back pain, but may increase the risk for acute lumbar disc herniation (LDH). Low back pain is a common early (prodromal) symptom of LDH and commonly precedes LDH diagnosis. Our objective was to investigate the association between chiropractic care and acute LDH with early surgical intervention, and contrast this with the association between primary care physician (PCP) care and acute LDH with early surgery.Methods: Using a self-controlled case series design and population-based healthcare databases in Ontario, Canada, we investigated all adults with acute LDH requiring emergency department (ED) visit and early surgical intervention from April 1994 to December 2004. The relative incidence of acute LDH with early surgery in exposed periods after chiropractic visits relative to unexposed periods was estimated within individuals, and compared with the relative incidence of acute LDH with early surgery following PCP visits.Results: 195 cases of acute LDH with early surgery (within 8 weeks) were identified in a population of more than 100 million person-years. Strong positive associations were found between acute LDH and both chiropractic and PCP visits. The risk for acute LDH with early surgery associated with chiropractic visits was no higher than the risk associated with PCP visits.Conclusions: Both chiropractic and primary medical care were associated with an increased risk for acute LDH requiring ED visit and early surgery. Our analysis suggests that patients with prodromal back pain from a developing disc herniation likely seek healthcare from both chiropractors and PCPs before full clinical expression of acute LDH. We found no evidence of excess risk for acute LDH with early surgery associated with chiropractic compared with primary medical care. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to describe time trends in response rates in case-control studies of cancer and identify study design factors that influence response rate.Methods: We reviewed 370 case-control studies of cancer published in 12 journals during indicator years in each of the last four decades. We estimated time trends of response rates and reasons for nonresponse in each of the following types of study subjects: cases, medical source controls, and population controls. We also estimated response rates according to characteristics of study context.Results: Median response rates among cases and population controls were between 75% and 80% in the 1970s. Between 1971 and 2010, study response rates declined by 0.31% per year for cases and 0.78% for population controls. Only a minority of studies reported reasons for nonparticipation; subject refusal was the most common reported reason. Studies conducted in North America had lower median response rates than studies conducted in Europe. In-person and telephone interviews elicited higher response rates than mail questionnaires.Conclusions: Response rates from case-control studies of cancer have declined, and this could threaten the validity of results derived from these studies. [ABSTRACT FROM AUTHOR]

In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995-2008). [ABSTRACT FROM AUTHOR]

Temporal variation in microbiome measurements can reduce statistical power in research studies. Quantification of this variation is essential for designing studies of chronic disease. We analyzed 16S ribosomal RNA profiles in paired biological specimens separated by 6 months from 3 studies conducted during 1985-2013 (a National Cancer Institute colorectal cancer study, a Costa Rica study, and the Human Microbiome Project). We evaluated temporal stability by calculating intraclass correlation coefficients (ICCs). Sample sizes needed in order to detect microbiome differences between equal numbers of cases and controls for a nested case-control design were calculated on the basis of estimated ICCs. Across body sites, 12 phylum-level ICCs were greater than 0.5. Similarly, 11 alpha-diversity ICCs were greater than 0.5. Fecal beta-diversity estimates had ICCs over 0.5. For a single collection with most microbiome metrics, detecting an odds ratio of 2.0 would require 300-500 cases when matching 1 case to 1 control at P = 0.05. Use of 2 or 3 sequential specimens reduces the number of required subjects by 40%-50% for low-ICC metrics. Relative abundances of major phyla and alpha-diversity metrics have low temporal stability. Thus, detecting associations of moderate effect size with the semetrics will require large sample sizes. Because beta diversity for feces is reasonably stable over time, smaller sample sizes can detect associations with community composition. Sequential prediagnostic specimens from thousands of prospectively ascertained cases are required to detectmodest disease associations with particular microbiomemetrics. [ABSTRACT FROM AUTHOR]

In longitudinal studies, standard analysis may yield biased estimates of exposure effect in the presence of time-varying confounders that are also intermediate variables. We aimed to quantify the relationship between obesity and coronary heart disease (CHD) by appropriately adjusting for time-varying confounders. This study was performed in a subset of participants from the Atherosclerosis Risk in Communities (ARIC) Study (1987-2010), a US study designed to investigate risk factors for atherosclerosis. General obesity was defined as body mass index (weight (kg)/height (m)2) ≥30, and abdominal obesity (AOB) was defined according to either waist circumference (≥102 cm in men and ≥88 cm in women) or waist:hip ratio (≥0.9 in men and ≥0.85 in women). The association of obesity with CHD was estimated by G-estimation and compared with results from accelerated failure-time models using 3 specifications. The first model, which adjusted for baseline covariates, excluding metabolic mediators of obesity, showed increased risk of CHD for all obesity measures. Further adjustment for metabolic mediators in the second model and time-varying variables in the third model produced negligible changes in the hazard ratios. The hazard ratios estimated by G-estimation were 1.15 (95% confidence interval (CI): 0.83, 1.47) for general obesity, 1.65 (95% CI: 1.35, 1.92) for AOB based on waist circumference, and 1.38 (95% CI: 1.13, 1.99) for AOB based on waist:hip ratio, suggesting that AOB increased the risk of CHD. The G-estimated hazard ratios for both measures were further from the null than those derived from standard models. [ABSTRACT FROM AUTHOR]

The field of epidemiology has been defined as the study of the spread and control of disease. However, epidemiology frequently focuses on studies of etiology and distribution of disease at the cost of understanding the best ways to control disease. Moreover, only a small fraction of scientific discoveries are translated into public health practice, and the process from discovery to translation is exceedingly slow. Given the importance of translational science, the future of epidemiologic training should include competency in implementation science, whose goal is to rapidly move evidence into practice. Our purpose in this paper is to provide epidemiologists with a primer in implementation science, which includes dissemination research and implementation research as defined by the National Institutes of Health. We describe the basic principles of implementation science, highlight key components for conducting research, provide examples of implementation studies that encompass epidemiology, and offer resources and opportunities for continued learning. There is a clear need for greater speed, relevance, and application of evidence into practice, programs, and policies and an opportunity to enable epidemiologists to conduct research that not only will inform practitioners and policymakers of risk but also will enhance the likelihood that evidence will be implemented. [ABSTRACT FROM AUTHOR]

Modeling risk-factor trajectories is critical to understanding the natural history of diseases, yet the measurement tools used to assess risk factors often evolve during follow-up in cohorts, and such change prevents longitudinal analyses using standard models. We addressed this issue with a latent process model. Trajectories of average intakes of 5 food families (fish, meat, fruits, vegetables, and carbohydrate-rich foods) were described in prodromal dementia during the 10 years prior to diagnosis of cases and compared with those of controls, using a case-control sample nested within the Three-City Study, Bordeaux, France (1999-2012). Food intakes were measured by 2 or 3 different subquestionnaires across 5 repeated food frequency questionnaires. The sample comprised 205 incident cases and 410 controls matched for age, sex, education, and number of repeated food frequency questionnaires. Intakes of fish, fruits, and vegetables decreased at the approach of diagnosis among cases, suggesting reverse causation. This study demonstrated that the latent process model approach constitutes a powerful framework for modeling risk-factor trajectories, even when measurement tools change sequentially over time. Coupled with a case-control approach to contrast trajectories in prodromal disease versus healthy status, it can help us to understand the dynamic, causal relationships between risk factors and diseases. [ABSTRACT FROM AUTHOR]

Longitudinal cohort studies can provide important evidence about preventable causes of disease, but the success relies heavily on the commitment of their participants, both at recruitment and during follow up. Initial participation rates have decreased in recent decades as have willingness to participate in subsequent follow ups. It is important to examine how such selection affects the validity of the results. In this article, we describe the conceptual framework for selection bias due to nonparticipation and loss to follow up in cohort studies, using both a traditional epidemiological approach and directed acyclic graphs. Methods to quantify selection bias are introduced together with analytical strategies to adjust for the bias including controlling for covariates associated with selection, inverse probability weighting and bias analysis. We use several studies conducted in the Danish National Birth Cohort as examples of how to quantify selection bias and also understand the underlying selection mechanisms. Although women who chose to participate in this cohort were typically of higher social status, healthier and with less disease than all those eligible for study, differential selection was modest and the influence of selection bias on several selected exposure-outcome associations was limited. These findings are reassuring and support enrolling a subset of motivated participants who would engage in long-term follow up rather than prioritize representativeness. Some of the presented methods are applicable even with limited data on nonparticipants and those lost to follow up, and can also be applied to other study designs such as case-control studies and surveys. [ABSTRACT FROM AUTHOR]

Confounding is an important source of bias, but it is often misunderstood. We consider how confounding occurs and how to address confounding using examples. Study results are confounded when the effect of the exposure on the outcome, mixes with the effects of other risk and protective factors for the outcome. This problem arises when these factors are present to different degrees among the exposed and unexposed study participants, but not all differences between the groups result in confounding. Thinking about an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe helps to distinguish confounders from other differences. In an actual study, an observed unexposed population is chosen to stand in for the unobserved parallel universe. Differences between this substitute population and the parallel universe result in confounding. Confounding by identified factors can be addressed analytically and through study design, but only randomization has the potential to address confounding by unmeasured factors. Nevertheless, a given randomized study may still be confounded. Confounded study results can lead to incorrect conclusions about the effect of the exposure of interest on the outcome. [ABSTRACT FROM AUTHOR]

Confounding biases study results when the effect of the exposure on the outcome mixes with the effects of other risk and protective factors for the outcome that are present differentially by exposure status. However, not all differences between the exposed and unexposed group cause confounding. Thus, sources of confounding must be identified before they can be addressed. Confounding is absent in an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe. In an actual study, an observed unexposed population represents the unobserved parallel universe. Thinking about differences between this substitute population and the unexposed parallel universe helps identify sources of confounding. These differences can then be represented in a diagram that shows how risk and protective factors for the outcome are related to the exposure. Sources of confounding identified in the diagram should be addressed analytically and through study design. However, treating all factors that differ by exposure status as confounders without considering the structure of their relation to the exposure can introduce bias. For example, conditions affected by the exposure are not confounders. There are also special types of confounding, such as time-varying confounding and unfixable confounding. It is important to evaluate carefully whether factors of interest contribute to confounding because bias can be introduced both by ignoring potential confounders and by adjusting for factors that are not confounders. The resulting bias can result in misleading conclusions about the effect of the exposure of interest on the outcome. [ABSTRACT FROM AUTHOR]

Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring at regular or irregular intervals, there is no consensus on how to handle person-time between participants' last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected. [ABSTRACT FROM AUTHOR]

Using imperfect tests may lead to biased estimates of disease frequency and of associations between risk factors and disease. For instance in longitudinal udder health studies, both quarters at risk and incident intramammary infections (IMI) can be wrongly identified, resulting in selection and misclassification bias, respectively. Diagnostic accuracy can possibly be improved by using duplicate or triplicate samples for identifying quarters at risk and, subsequently, incident IMI. The objectives of this study were to evaluate the relative impact of selection and misclassification biases resulting from IMI misclassification on measures of disease frequency (incidence) and of association with hypothetical exposures. The effect of improving the sampling strategy by collecting duplicate or triplicate samples at first or second sampling was also assessed. Data sets from a hypothetical cohort study were simulated and analyzed based on a separate scenario for two common mastitis pathogens representing two distinct prevailing patterns. Staphylococcus aureus , a relatively uncommon pathogen with a low incidence, is identified with excellent sensitivity and almost perfect specificity. Coagulase negative staphylococci (CNS) are more prevalent, with a high incidence, and with milk bacteriological culture having fair Se but excellent Sp. The generated data sets for each scenario were emulating a longitudinal cohort study with two milk samples collected one month apart from each quarter of a random sample of 30 cows/herd, from 100 herds, with a herd-level exposure having a known strength of association. Incidence of IMI and measure of association with exposure (odds ratio; OR) were estimated using Markov Chain Monte Carlo (MCMC) for each data set and using different sampling strategies (single, duplicate, triplicate samples with series or parallel interpretation) for identifying quarters at risk and incident IMI. For S. aureus biases were small with an observed incidence of 0.29 versus a true incidence of 0.25 IMI/100 quarter-month. In the CNS scenario, diagnostic errors in the two samples led to important selection (40 IMI/100 quarter-month) and misclassification (23 IMI/100 quarter-month) biases for estimation of IMI incidence, respectively. These biases were in opposite direction and therefore the incidence measure obtained using single sampling on both the first and second test (29 IMI/100 quarter-month) was exactly the true value. In the S. aureus scenario the OR for association with exposure showed little bias (observed OR of 3.1 versus true OR of 3.2). The CNS scenario revealed the presence of a large misclassification bias moving the association towards the null value (OR of 1.7 versus true OR of 2.6). Little improvement could be brought using different sampling strategies aiming at improving Se and/or Sp on first and/or second sampling or using a two out of three interpretation for IMI definition. Increasing number of samples or tests can prevent bias in some situations but efforts can be spared by holding to a single sampling approach in others. When designing longitudinal studies, evaluating potential biases and best sampling strategy is as critical as the choice of test. [ABSTRACT FROM AUTHOR]

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. Inmeta-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. [ABSTRACT FROM AUTHOR]

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 statisticalmethods 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. [ABSTRACT FROM AUTHOR]

Misclassification of body mass index (BMI) categories arising from self-reported weight and height can bias hazard ratios in studies of BMI and mortality. We examined the effects on hazard ratios of such misclassification using national US survey data for 1976 through 2010 that had both measured and self-reported weight and height along with mortality follow-up for 48,763 adults and a subset of 17,405 healthy never-smokers. BMI was categorized as <22.5 (low), 22.5-24.9 (referent), 25.0-29.9 (overweight), 30.0-34.9 (class I obesity), and ≥35.0 (class II-III obesity). Misreporting at higher BMI categories tended to bias hazard ratios upwards for those categories, but that effect was augmented, counterbalanced, or even reversed by misreporting in other BMI categories, in particular those that affected the reference category. For example, among healthy male never-smokers, misclassifications affecting the overweight and the reference categories changed the hazard ratio for overweight from 0.85 with measured data to 1.24 with self-reported data. Both the magnitude and direction of bias varied according to the underlying hazard ratios in measured data, showing that findings on bias from one study should not be extrapolated to a study with different underlying hazard ratios. Because of misclassification effects, self-reported weight and height cannot reliably indicate the lowest-risk BMI category. [ABSTRACT FROM AUTHOR]

Background: Chronic diseases are highly prevalent and cluster in individuals (multimorbidity). This study investigated the association between multimorbidity and Health-Related Quality of Life (HRQoL), assessing the combination of chronic diseases highly correlated with this outcome.Methods: We conducted a household survey in 2015 in a random sample of 2912 South Australian adults (48.9 ± 18.1 years; 50.9% females), obtaining information on sociodemographics, lifestyle, and 17 chronic conditions clustered in four different groups (metabolic, cardiovascular, gastrointestinal, and musculoskeletal). Information on physical (PCS) and mental components scores (MCS) of HRQoL were assessed using the SF-12 questionnaire. Multivariable linear regression models considering individual diseases (mutually adjusted) and clusters within- and between-groups were used to test the associations.Results: Only 41% of the sample was negative for all the investigated diseases. The most prevalent conditions were osteoarthritis, obesity and hypertension, which affected one in every four individuals. PCS was markedly lower among those reporting stroke, heart failure, and osteoarthritis, but they were not associated with MCS. Direct-trend relationships were observed between the number of chronic conditions (clusters within- and between-groups) and PCS, but not with MCS. The strongest association with PCS was for musculoskeletal conditions (difference between those affected by 2+ conditions and those free of these conditions -6.7 95%CI -8.5;-5.4), and lower PCS were observed in any combination of clusters between-group including musculoskeletal diseases.Conclusion: In the context of multimorbidity, musculoskeletal diseases are a key determinant group of PCS, amplifying the association of other chronic conditions on physical but not on mental health. [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. [ABSTRACT FROM AUTHOR]

Purpose Estimate incidence of autoimmune conditions in a population who received HPV4 vaccine and a comparison unvaccinated population. Electronic health record (EHR) data may contain inaccurate or incomplete coding, while manual chart review of all cases may not be feasible. We propose a method to estimate incidence using EHR data and case review for a sample. Methods Suspected incident cases were identified using ICD-9 codes, laboratory results and medications related to the condition. A random sample of charts was reviewed to confirm the diagnosis and determine disease onset date. Multiple imputation, using a Monte Carlo model including age and disease indicators was used to impute case status of non-reviewed cases. Incidence rate was calculated in each imputed dataset, with median and percentiles giving a distribution for the estimated incidence rate. Sensitivity analyses compared modeled results to results without imputation and results where imputation was applied to the subset of cases identified using specific ICD-9 codes. Results The model accounted for differential case confirmation rates by age and method of case identification, identifying a potential safety signal that was missed relying on EHR data alone. Conclusions This method may be useful for computing incidence when full case review is not feasible. [ABSTRACT FROM AUTHOR]

Background: People with intellectual disability are difficult to retain in longitudinal studies. Research on determinants of study retention for individual-carer dyads, and their reasons for drop-out, are limited.Aims: To investigate characteristics associated with drop-out, and to investigate whether characteristics varied by stage of drop-out.Methods and Procedures: Data are from an Australian randomized trial with adolescents with intellectual disability living in the community. Characteristics of both the adolescent and their nominated carer were collected at baseline. Carers were sent an exit questionnaire approximately two years after enrolment.Outcomes and Results: Baseline information was available for 566 adolescents: 72(13.0%) withdrew during the study, and 96(17.3%) didn't return exit questionnaires. Characteristics associated with drop-out during the study were being in the intervention group, the carer being younger, and the carer not being one of the adolescent's parents. Characteristics associated with withdrawal at exit were carer having lower education and carer having lower socioeconomic status. No adolescent characteristic was associated with drop-out.Conclusions and Implications: Characteristics of drop-outs weren't related to the adolescent and differed according to timing. Drop-out during the study was associated with study burden, whereas characteristics of drop-outs at exit interview were associated with lower social position. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Increasingly, the statistical and epidemiologic literature is focusing beyond issues of internal validity and turning its attention to questions of external validity. Here, we discuss some of the challenges of transporting a causal effect from a randomized trial to a specific target population. We present an inverse odds weighting approach that can easily operationalize transportability. We derive these weights in closed form and illustrate their use with a simple numerical example. We discuss how the conditions required for the identification of internally valid causal effects are translated to apply to the identification of externally valid causal effects. Estimating effects in target populations is an important goal, especially for policy or clinical decisions. Researchers and policy-makers should therefore consider use of statistical techniques such as inverse odds of sampling weights, which under careful assumptions can transport effect estimates from study samples to target populations. [ABSTRACT FROM AUTHOR]

In the last few years, stakeholders in the scientific community have raised alarms about a perceived lack of reproducibility of scientific results. In reaction, guidelines for journals have been promulgated and grant applicants have been asked to address the rigor and reproducibility of their proposed projects. Neither solution addresses a primary culprit, which is the culture of null hypothesis significance testing that dominates statistical analysis and inference. In an innovative research enterprise, selection of results for further evaluation based on null hypothesis significance testing is doomed to yield a low proportion of reproducible results and a high proportion of effects that are initially overestimated. In addition, the culture of null hypothesis significance testing discourages quantitative adjustments to account for systematic errors and quantitative incorporation of prior information. These strategies would otherwise improve reproducibility and have not been previously proposed in the widely cited literature on this topic. Without discarding the culture of null hypothesis significance testing and implementing these alternative methods for statistical analysis and inference, all other strategies for improving reproducibility will yield marginal gains at best. [ABSTRACT FROM AUTHOR]

Observational studies and randomized controlled trials of menopausal hormone therapy (HT) and chronic disease risk appear to have divergent results for cardiovascular disease. However, differences may be related to a modifying effect of age, time since menopause, and HT formulation. In the Nurses' Health Study (NHS) (enrolling during 1980-1994 and following participants until 2002), we investigated associations between the use of oral conjugated equine estrogens (CEE) (0.625 mg/day) plus medroxyprogesterone acetate (MPA) (<10 mg/day) or oral CEE alone and cardiovascular disease, cancer, all-cause mortality, and other major endpoints among postmenopausal women, aged 50-79 years at HT initiation. Among women aged 50-59 years at HT initiation, associations of CEE alone or CEE+MPA with most clinical outcomes were highly concordant between NHS and Women's Health Initiative (WHI). However, for myocardial infarction, results for CEE+MPA were in the direction of risk elevation in WHI and in the direction of risk reduction in NHS. When examined according to years since menopause onset (<10 years) rather than age group, results were nonsignificant and concordant for both studies. Because few women in the NHS initiated HT after age 60 years, we did not examine associations in this group. Discrepancies between NHS and WHI could largely be attributed to differences in the age structure of the populations and age at HT initiation. [ABSTRACT FROM AUTHOR]

Objectives To investigate whether height at the age of 31 is associated with the incidence of knee and hip osteoarthritis ( OA) in the following 15 years. Methods Participants in The Northern Finland Birth Cohort 1966 ( NFBC1966) diagnosed with knee or hip OA between the ages of 31 and 46 were used as OA cases. Study subjects without knee and hip OA were used as the controls. Height and weight were measured in a clinical examination at the age of 31 (baseline). Mean heights for the OA cases and the controls were compared by an independent samples t-test. Cox regression analysis was performed to calculate the risk for OA for different height quartiles. The results were adjusted for body mass index/weight, education, smoking and leisure-time physical activity at baseline. Additionally, a Kaplan-Meier analysis was performed. Results Men with knee OA were 2.6 cm taller ( P < 0.001) and women with knee OA 1.2 cm taller ( P = 0.048) than the controls. Hip OA cases were found to be slightly shorter than the controls, but no statistically significant differences were observed. The adjusted hazard ratios ( HRs) for knee OA and hip OA in the highest quartile were 2.5 (95% CI 1.4-4.5) and 1.0 (95% CI 0.3-3.4) for men and 1.8 (95% CI 1.0-3.1) and 0.7 (95% CI 0.2-2.3) for women. Conclusions Height at the age of 31 was associated with incidence of early knee OA, diagnosed prior to age 46. However, the low incidence of hip OA made our results for hip OA inconclusive. [ABSTRACT FROM AUTHOR]

Prospective cohort studies are important tools for identifying causes of disease. However, these studies are susceptible to attrition. When information collected after enrollment is through interview or exam, attrition leads to missing information for nonrespondents. The Agricultural Health Study enrolled 52,394 farmers in 1993-1997 and collected additional information during subsequent interviews. Forty-six percent of enrolled farmers responded to the 2005-2010 interview; 7% of farmers died prior to the interview. We examined whether response was related to attributes measured at enrollment. To characterize potential bias from attrition, we evaluated differences in associations between smoking and incidence of 3 cancer types between the enrolled cohort and the subcohort of 2005-2010 respondents, using cancer registry information. In the subcohort we evaluated the ability of inverse probability weighting (IPW) to reduce bias. Response was related to age, state, race/ethnicity, education, marital status, smoking, and alcohol consumption. When exposure and outcome were associated and case response was differential by exposure, some bias was observed; IPW conditional on exposure and covariates failed to correct estimates. When response was nondifferential, subcohort and full-cohort estimates were similar, making IPW unnecessary. This example provides a demonstration of investigating the influence of attrition in cohort studies using information that has been self-reported after enrollment. [ABSTRACT FROM AUTHOR]

No clinical risk score is universally accepted for coronary artery disease. In 603 patients (mean age, 61.2 ± 12.3 yr) with stable coronary artery disease, we investigated the predictive power of clinical risk scores derived from the Framingham, the Long-term Intervention with Pravastatin in Ischemic Disease (LIPID), and the Vienna and Ludwigshafen Coronary Artery Disease (VILCAD) studies. Secondary outcomes were the recurrence of an acute thrombotic event (coronary events, strokes, or transient ischemic attacks), or heart failure or death. The primary outcome was the combination of secondary outcomes. During follow-up (duration, 2.08 ± 0.97 yr), 42 patients had an acute thrombotic event; 22, heart failure or death; and 60, the primary outcome. The Framingham score predicted acute thrombotic events: hazard ratio (HR)=1.05; 95% confidence interval (CI), 1.01-1.08; P=0.03; net reclassification index (NRI, calculated to evaluate improvement in prediction gained by adding different risk scores to models constructed with variables excluded from the calculation of that score)=9.7% (95% CI, 9.6-9.8). The LIPID (HR=1.13; 95% CI, 1.04-1.22; P=0.005) and VILCAD scores (HR=1.99; 95% CI, 1.48-2.67; P <0.001) predicted heart failure or death with NRIs of 5.8% (95% CI, 5.7-5.9) and 18.6% (95% CI, 18.3-18.9), respectively. The primary outcome was predicted by the LIPID (HR=1.1; 95% CI, 1.03-1.17; P=0.005) and VILCAD scores (HR=1.39; 95% CI, 1.13-1.70; P=0.003). The NRIs (95% CIs) were 3.4% (3.3-3.5) and 19.4% (19.3-19.6), respectively. We conclude that the accuracy of these risk scores varies in accordance with the outcome studied. [ABSTRACT FROM AUTHOR]

Objective: Both computerised and telephone surveys have potential advantages for research data collection. The current study aimed to determine the: (i) feasibility, (ii) acceptability, and (iii) cost per completed survey of computer tablet versus telephone data collection for clients attending an outpatient drug and alcohol treatment clinic.Design: Two-arm randomised controlled trial.Method: Clients attending a drug and alcohol outpatient clinic in New South Wales, Australia, were randomised to complete a baseline survey via computer tablet in the clinic or via telephone interview within two weeks of their appointment. All participants completed a three-month follow-up survey via telephone.Results: Consent and completion rates for the baseline survey were significantly higher in the computer tablet condition. The time taken to complete the computer tablet survey was lower (11min) than the telephone condition (17min). There were no differences in the proportion of consenters or completed follow-up surveys between the two conditions at the 3-month follow-up. Acceptability was high across both modes of data collection. The cost of the computer tablet condition was $67.52 greater per completed survey than the telephone condition.Conclusion: There is a trade-off between computer tablet and telephone data collection. While both data collection methods were acceptable to participants, the computer tablet condition resulted in higher consent and completion rates at baseline, therefore yielding greater external validity, and was quicker for participants to complete. Telephone data collection was however, more cost-effective. Researchers should carefully consider the mode of data collection that suits individual study needs. [ABSTRACT FROM AUTHOR]

Neighborhood conditions may influence a broad range of health indicators, including obesity, injury, and psychopathology. In particular, neighborhood physical disorder--a measure of urban deterioration--is thought to encourage crime and high-risk behaviors, leading to poor mental and physical health. In studies to assess neighborhood physical disorder, investigators typically rely on time-consuming and expensive in-person systematic neighborhood audits. We compared 2 audit-based measures of neighborhood physical disorder in the city of Detroit, Michigan: One used Google Street View imagery from 2009 and the other used an in-person survey conducted in 2008. Each measure used spatial interpolation to estimate disorder at unobserved locations. In total, the virtual audit required approximately 3% of the time required by the in-person audit. However, the final physical disorder measures were significantly positively correlated at census block centroids (r = 0.52), identified the same regions as highly disordered, and displayed comparable leave-one-out cross-validation accuracy. The measures resulted in very similar convergent validity characteristics (correlation coefficients within 0.03 of each other). The virtual audit-based physical disorder measure could substitute for the in-person one with little to no loss of precision. Virtual audits appear to be a viable and much less expensive alternative to in-person audits for assessing neighborhood conditions. [ABSTRACT FROM AUTHOR]