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Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002-2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing by fitting a dynamic harmonic regression (DHR) model to salbutamol prescribing in relation to temperature. We compared the output of DHR models to static sinusoidal regression models. We used the DHR-fitted values as an offset in mixed-effects models that aimed to account for the remaining spatiotemporal variation in prescribing rates. As diagnostic checks, we assessed spatial and temporal correlation separately and jointly. Our application of a DHR model resulted in abetter fit to the seasonal variation of prescribing than was obtained with a static model. After adjusting for the fitted values from the DHR model, we did not detect any remaining spatiotemporal correlation in the model's residuals. Using a DHR model and temperature data to account for the periodicity of prescribing proved to be an efficient way to capture its seasonal variation. The diagnostic procedures indicated that there was no need to model any remaining correlation explicitly. [ABSTRACT FROM AUTHOR]

Introduction: Dietary assessment methods are an important instrument for nutrition research. Food frequency questionnaires (FFQs) have been the most frequently used dietary assessment tool in epidemiological and intervention studies. There is a great necessity for new methods of determination of habitual dietary intake that overcome the limitations of these traditional methods.Objectives: The objectives of the present study were to develop a new method, based on an application for mobile phones called e-EPIDEMIOLOGY, designed to record individual consumption data about a short series of foods/drinks, and to compare data collected using this tool with those obtained from a previously validated short paper FFQ.Methods: University students over 18 years recorded the consumption of certain foods/drinks using e-EPIDEMIOLOGY during 28 consecutive days and then filled out a short paper FFQ at the end of the study period. To evaluate the agreement between both methods, Spearman's correlation coefficient, cross-classification analysis and a weighted kappa statistic were used.Results: One hundred and nineteen participants completed the study (71.4% female and 28.6% male). The mean Spearman's correlation coefficients for food/drink group intake between the two methods was 0.73. The mean percentage of participants cross-classified into categories of "exact agreement + adjacent" was 91.6%. The average weighted kappa statistic was 0.60.Conclusions: The results indicate that e-EPIDEMIOLOGY has good agreement with the previously validated FFQ short paper. However, it was noted that further testing of e-EPIDEMIOLOGY is required to establish its wider utility. [ABSTRACT FROM AUTHOR]

Background: Cause-specific (CS) and net survival in a relative survival framework (RS) are two of the most common methods for estimating cancer survival. In this paper, we assess the differences in results produced by two permutations of cause-specific and relative survival applied to estimating cancer survival and disparities in cancer survival, using data from First Nations and non-Aboriginal populations in Canada. Methods: Subjects were members of the 1991 Canadian Census Mortality Cohort, a population-based cohort of adult respondents to the 1991 Long Form Census who have been followed up for incident cancers and death through linkage to administrative databases. We compared four methods: relative survival analyses with ethnicity-specific life tables (RS-ELT); relative survival with general population life tables (RS-GLT); cause-specific survival with a broad definition of cancer death (CS-Broad); and cause-specific survival with a narrow definition of cause of death (CS-Narrow) and applied these to the nine most common cancers among First Nations. Results: Apart from breast and prostate cancers, RS-ELT, RS-GLT, and CS-Broad tended to produce similar estimates of age-standardized five-year survival, whereas CS-Narrow yielded higher estimates of survival. CS-Narrow estimates were particularly unlike those based on the other methods for cancers of the digestive and respiratory tracts. Estimates of disparities in survival were generally comparable across the four methods except for breast and prostate cancers. Conclusions: Cancer surveillance efforts in sub-populations defined by race, ethnicity, geography, socioeconomic status, or similar factors are necessary for identifying disparities and monitoring progress toward reducing them. In the absence of routine monitoring of cancer survival and cancer survival disparities in these populations, estimates generated by different methods will inevitably be compared over time and across populations. In this study, we demonstrate that caution should be exercised in making these comparisons, particularly in interpreting cause-specific survival rates with an unknown or narrow definition of cancer death and in estimates of breast and prostate cancer survival and/or disparities in survival generated by different methods. [ABSTRACT FROM AUTHOR]

In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case–control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case–control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods. [ABSTRACT FROM AUTHOR]

Heart failure with preserved ejection fraction is often preceded by diastolic dysfunction (DD). Of several published DD criteria, it is unclear which, if any, are applicable to data obtained in epidemiologic cohorts. We evaluated the prevalence of DD using previously published definitions in a population-based study, the Coronary Artery Risk Development in Young Adults (CARDIA) Study, using data gathered in 2010-2011. Echocardiography was performed on 3,474 individuals (mean age = 50.2 years) at the CARDIA year 25 examination. Four published definitions of DD were studied. We calculated DD prevalence for each definition and determined the overlap between definitions. We used logistic regression to assess the strength of associations between demographic and clinical factors and the definitions of DD. Prevalence of DD ranged from 2% to 32% across the 4 definitions, with a minority of cases identified by more than 1 definition. Two definitions classified 38%-39%of the study sample as indeterminate for DD. Associations of risk factors with DD varied considerably, with male sex being associated positively with DD for one definition (odds ratio = 1.4, 95% confidence interval: 1.2, 1.6) and inversely for another (odds ratio = 0.7, 95% confidence interval: 0.6, 0.8). Prevalence of DD varies markedly in CARDIA by the definition applied. A uniform, reliable, and accurate definition of DD for epidemiologic studies is needed. [ABSTRACT FROM AUTHOR]

Background A method applied in some large studies of weight and mortality is to begin with a well-defined analytic cohort and use successive restrictions to control for methodologic bias and arrive at final analytic results. Materials and methods Two observational studies of body mass index and mortality allow a comparative assessment of these restrictions in very large data sets. One was a meta-analysis of individual participant data with a sample size of 8 million. The second was a study of a South Korean cohort with a sample size of 12 million. Both presented results for participants without pre-existing disease before and after restricting the sample to never-smokers and deleting the first 5 years of follow-up. Results Initial results from both studies were generally similar, with hazard ratios ( HRs) below 1 for overweight and above 1 for underweight and obesity. The meta-analysis showed higher HRs for overweight and obesity after the restrictions, including a change in the direction of the HR for overweight from 0·99 (95% CI: 0·98-1·01) to 1·11 (95% CI: 1·10, 1·11). The South Korean data showed little effect of the restrictions and the HR for overweight changed from 0·85 (95% CI: 0·84-0·86) to 0·91 (95% CI: 0·90, 0·91). The summary effect size for overweight was 0·90 (95% CI: 0·89-0·91) before restrictions and 1·02 (95% CI: 1·02, 1·03) after restrictions. Conclusions The effect of the restrictions is not consistent across studies, weakening the argument that analyses without such restrictions lack validity. [ABSTRACT FROM AUTHOR]

Background: The number needed to treat (NNT) is an absolute effect measure that has been used to assess beneficial and harmful effects of medical interventions. Several methods can be used to calculate NNTs, and they should be applied depending on the different study characteristics, such as the design and type of variable used to measure outcomes. Whether or not the most recommended methods have been applied to calculate NNTs in studies published in the medical literature is yet to be determined. The aim of this study is to assess whether the methods used to calculate NNTs in studies published in medical journals are in line with basic methodological recommendations.Methods: The top 25 high-impact factor journals in the "General and/or Internal Medicine" category were screened to identify studies assessing pharmacological interventions and reporting NNTs. Studies were categorized according to their design and the type of variables. NNTs were assessed for completeness (baseline risk, time horizon, and confidence intervals [CIs]). The methods used for calculating NNTs in selected studies were compared to basic methodological recommendations published in the literature. Data were analyzed using descriptive statistics.Results: The search returned 138 citations, of which 51 were selected. Most were meta-analyses (n = 23, 45.1%), followed by clinical trials (n = 17, 33.3%), cohort (n = 9, 17.6%), and case-control studies (n = 2, 3.9%). Binary variables were more common (n = 41, 80.4%) than time-to-event (n = 10, 19.6%) outcomes. Twenty-six studies (51.0%) reported only NNT to benefit (NNTB), 14 (27.5%) reported both NNTB and NNT to harm (NNTH), and 11 (21.6%) reported only NNTH. Baseline risk (n = 37, 72.5%), time horizon (n = 38, 74.5%), and CI (n = 32, 62.7%) for NNTs were not always reported. Basic methodological recommendations to calculate NNTs were not followed in 15 studies (29.4%). The proportion of studies applying non-recommended methods was particularly high for meta-analyses (n = 13, 56.5%).Conclusions: A considerable proportion of studies, particularly meta-analyses, applied methods that are not in line with basic methodological recommendations. Despite their usefulness in assisting clinical decisions, NNTs are uninterpretable if incompletely reported, and they may be misleading if calculating methods are inadequate to study designs and variables under evaluation. Further research is needed to confirm the present findings. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the reproducibility and validity of a 61-item semiquantitative food frequency questionnaire used in a large prospective study among women. This form was administered twice to 173 participants at an interval of approximately one year (1980-1981), and four one-week diet records for each subject were collected during that period. Intradass correlation coefficients for nutrient intakes estimated by the one-week diet records (range = 0.41 for total vitamin A without supplements to 0.79 for vitamin B6 with supplements) were similar to those computed from the questionnaire (range = 0.49 for total vitamin A without supplements to 0.71 for sucrose), indicating that these methods were generally comparable with respect to reproducibility. With the exception of sucrose and total carbohydrate, nutrient intakes from the diet records tended to correlate more strongly with those computed from the questionnaire after adjustment for total caloric intake. Correlation coefficients between the mean calorie-adjusted intakes from the four one-week diet records and those from the questionnaire completed after the diet records ranged from 0.36 for vitamin A without supplements to 0.75 for vitamin C with supplements. Overall, 48% of subjects in the lowest quintile of calorie-adjusted intake computed from the diet records were also in the lowest questionnaire quintile, and 74% were in the lowest one of two questionnaire quintiles. Similarly, 49% of those in the highest diet record quintile were also in the highest questionnaire quintile, and 77% were in the highest one or two questionnaire quintiles. These data indicate that a simple self-administered dietary questionnaire can provide useful information about individual nutrient intakes over a one-year period. [ABSTRACT FROM AUTHOR]

A case-control design involving only cases may be used when brief exposure causes a transient change in risk of a rare acute-onset disease. The design resembles a retrospective nonrandomized crossover study but differs in having only a sample of the base population-time. The average incidence rate ratio for a hypothesized effect period following the exposure is estimable using the Mantel-Haenszel estimator. The duration of the effect period is assumed to be that which maximizes the rate ratio estimate. Self-matching of cases eliminates the threat of control-selection bias and increases efficiency. Pilot data from a study of myocardial infarction onset illustrate the control of within-individual confounding due to temporal association of exposures. Am J Epidemioi 1991:133:144-53. [ABSTRACT FROM AUTHOR]

Intuitively, researchers do not include subjects who do not have the opportunity to be exposed, such as men in studies on oral contraceptives (OCs). We aimed to explore in which situations it is nevertheless beneficial to do so. We considered the effect of including men in case-control analyses of 8 different hypothetical data sets on the effect of OC use and venous thrombosis. In all scenarios, OC use was the exposure of interest, sex the factor that determined exposure opportunity, and air travel another risk factor. In some of these scenarios, sex and air travel were included as confounders or effect modifiers. Logistic regression was used to estimate odds ratios. Standard errors of the estimated log odds ratios, including and excluding men, were compared. We also studied the effect of including men using data from 1999-2004 from a case-control study on risk factors for venous thrombosis, conducted in the Netherlands. In all hypothetical examples, and in the real-data study, addition of men to the analysis yielded the same odds ratios when correctly adjusting for confounding. Moreover, use of additional subjects often led to more precise estimates. We suggest that subjects who do not have the opportunity to be exposed should not routinely be excluded from epidemiologic studies. [ABSTRACT FROM AUTHOR]

A propensity score (PS) model's ability to control confounding can be assessed by evaluating covariate balance across exposure groups after PS adjustment. The optimal strategy for evaluating a disease risk score (DRS) model's ability to control confounding is less clear. DRS models cannot be evaluated through balance checks within the full population, and they are usually assessed through prediction diagnostics and goodness-of-fit tests. A proposed alternative is the "dry-run" analysis, which divides the unexposed population into "pseudo-exposed" and "pseudo-unexposed" groups so that differences on observed covariates resemble differences between the actual exposed and unexposed populations. With no exposure effect separating the pseudo-exposed and pseudounexposed groups, a DRS model is evaluated by its ability to retrieve an unconfounded null estimate after adjustment in this pseudo-population. We used simulations and an empirical example to compare traditional DRS performance metrics with the dry-run validation. In simulations, the dry run often improved assessment of confounding control, compared with the C statistic and goodness-of-fit tests. In the empirical example, PS and DRS matching gave similar results and showed good performance in terms of covariate balance (PS matching) and controlling confounding in the dry-run analysis (DRS matching). The dry-run analysis may prove useful in evaluating confounding control through DRS models. [ABSTRACT FROM AUTHOR]

Background: Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematically impossible.Methods: In this paper, I derive bounding formulae for sufficient-cause interactions under the assumption of no redundancy.Results: Two real data sets are used to demonstrate the method (R codes provided). The proposed bounds are sharp and sharper than previous bounds.Conclusions: Sufficient-cause interactions can be quantified by setting bounds on them. [ABSTRACT FROM AUTHOR]

In previous studies, we found modestly decreased and increased risks of second breast cancer events with the use of statins and antibiotics, respectively, after adjustment for surveillance mammography. We evaluated detection bias by comparing receipt of surveillance mammography among users of these 2 disparate classes of medication. Adult women diagnosed with early-stage breast cancer during 1990-2008 (n = 3,965) while enrolled in an integrated health-care plan (Group Health Cooperative; Washington State) were followed for up to 10 years in the Commonly Used Medications and Breast Cancer Outcomes (COMBO) Study. Categories of antibiotic use included infrequent (1-3 dispensings/12 months) and frequent (≥4 dispensings/12 months) use, and categories of statin use included less adherent (1 dispensing/6 months) and adherent (≥2 dispensings/6 months). We examined associations between medication use and surveillance mammography using multivariable generalized estimating equations and evaluated the impact of adjusting for surveillance within Cox proportional hazard models. Frequent antibiotic users were less likely to receive surveillance mammography (odds ratio (OR) = 0.90, 95% confidence interval (CI): 0.82, 0.99) than were nonusers; no association was found among infrequent users (OR = 0.96, 95% CI: 0.90, 1.03). Adherent statin use was associated with more surveillance compared with nonuse (OR = 1.11, 95% CI: 1.01,1.25), but less adherent statin use was not (OR = 1.03, 95% CI: 0.81,1.31). Nodifference in associations between medications of interest and second breast cancer events was observed when surveillance was removed from otherwise adjusted models. The influence of detection bias by medication use warrants further exploration. [ABSTRACT FROM AUTHOR]

Incorrectly handling missing data can lead to imprecise and biased estimates. We describe the effect of applying different approaches to handling missing data in an analysis of the association between body mass index and all-cause mortality among people with type 2 diabetes. We used data from the Scottish diabetes register that were linked to hospital admissions data and death registrations. The analysis was based on people diagnosed with type 2 diabetes between 2004 and 2011, with follow-up until May 31,2014. The association between body mass index and mortality was investigated using Cox proportional hazards models. Findings were compared using 4 different missing-data methods: complete-case analysis, 2 multiple-imputation models, and nearest-neighbor imputation. There were 124,451 cases of type 2 diabetes, among which there were 17,085 deaths during 787,275 person-years of follow-up. Patients with missing data (24.8%) had higher mortality than those without missing data (adjusted hazard ratio = 1.36, 95% confidence interval: 1.31, 1.41). A U-shaped relationship between body mass index and mortality was observed, with the lowest hazard ratios occurring among moderately obese people, regardless of the chosen approach for handling missing data. Missing data may affect absolute and relative risk estimates differently and should be considered in analyses of routinely collected data. [ABSTRACT FROM AUTHOR]

Power and sample size considerations are critical for the design of epidemiologic studies of gene-environment interactions. Hwang et al. (Am J Epidemiol 1994; 140:1029-37) and Foppa and Spiegelman {Am J Epidemiol 1997; 146:596-604) have presented power and sample size calculations for case-control studies of gene- environment interactions. Comparisons of calculations using these approaches and an approach for general multivariate regression models for the odds ratio previously published by Lubin and Gail (Am J Epidemiol 1990; 131:552-66) have revealed substantial differences under some scenarios. These differences are the result of a highly restrictive characterization of the null hypothesis in Hwang et al. and Foppa and Spiegelman, which results in an underestimation of sample size and overestimation of power for the test of a gene-environment interaction. A computer program to perform sample size and power calculations to detect additive or multiplicative models of gene-environment interactions using the Lubin and Gail approach will be available free of charge in the near future from the National Cancer Institute. Am J Epidemiol 1999; 149:689-92. [ABSTRACT FROM AUTHOR]

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemio-logic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed healthcare databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases. [ABSTRACT FROM AUTHOR]

Objectives We use dispensing claims to benchmark prescriber access patterns for opioids against statins (a drug class with no known abuse potential) in Australia and British Columbia, Canada. Methods We used pharmaceutical claims from the Australian Pharmaceutical Benefits Scheme and British Columbian PharmaCare. We used individual-level claims in adults with complete capture of subsidized prescription drugs and ≥1 opioid or statin dispensing(s) in 2011. We report the distribution of unique opioid and statin prescribers overall, by age and jurisdiction. Key findings In 2011, the vast majority of persons visited one or two prescribers to access opioids and statins (Australia: 86.0% versus 91.5%; British Columbia: 90.2% versus 92.1% respectively). The proportion of persons accessing ≥4 prescribers was higher for persons dispensed opioids compared to statins (Australia: 6.5% versus 1.3%; British Columbia: 4.2% versus 1.7% respectively). For opioids, a greater proportion of persons aged <65 years accessed ≥4 opioid prescribers compared to their older counterparts (≥65 years); these differences were more pronounced in British Columbians receiving income assistance. Conclusions Benchmarking the entire spectrum of use for specific prescription drug classes could provide a useful tool for informing postmarket surveillance activities and may identify potentially problematic drug access behaviour(s). [ABSTRACT FROM AUTHOR]

Background: The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions. Methods to cluster adherence data: This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster ("cluster 1" = very low adherence; "cluster 2" = low adherence; "cluster 3" = moderate adherence; "cluster 4" = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data. Three illustrative cases: We present three cases to illustrate the use of the proposed clustering method. Conclusion: This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months' worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes. [ABSTRACT FROM AUTHOR]

Background: The central role of prostate-specific antigen (PSA) testing in the diagnosis of prostate cancer leads to the possibility that observational studies that report associations between risk factors and prostate cancer could be affected by detection bias. This study aims to investigate whether reported risk factors for prostate cancer are associated with PSA testing in a large middle-aged population-based cohort in the UK.Methods: The cross-sectional association between a wide range of sociodemographic, lifestyle, dietary and health characteristics with PSA testing was examined in 212,039 men aged 40-69 years in UK Biobank.Results: A total of 62,022 (29%) men reported they had ever had a PSA test. A wide range of factors was associated with a higher likelihood of PSA testing including age, height, education level, family history of prostate cancer, black ethnic origin, not being in paid/self-employment, living with a wife or partner, having had a vasectomy, being diagnosed with cancer or hypertension and having a high dietary intake of cereal, cooked and salad/raw vegetables, fresh fruit and tea. Conversely, socioeconomic deprivation, Asian ethnic origin, current smoking, low alcohol intake, high body-mass index, high coffee consumption and being diagnosed with diabetes, heart disease or stroke were associated with a lower likelihood of PSA testing.Conclusions: A variety of sociodemographic, lifestyle and health-related characteristics are associated with PSA testing, suggesting that observed associations of some of these traits with risk for prostate cancer in epidemiological studies may be, at least partially, due to detection bias. [ABSTRACT FROM AUTHOR]

Although randomized placebo-controlled trials (RCT) are critical to establish efficacy of vaccines at the time of licensure, important remaining questions about vaccine effectiveness (VE)-used here to include individual-level measures and population-wide impact of vaccine programmes-can only be answered once the vaccine is in use, from observational studies. However, such studies are inherently at risk for bias. Using a causal framework and illustrating with examples, we review newer approaches to detecting and avoiding confounding and selection bias in three major classes of observational study design: cohort, case-control and ecological studies. Studies of influenza VE, especially in seniors, are an excellent demonstration of the challenges of detecting and reducing such bias, and so we use influenza VE as a running example. We take a fresh look at the time-trend studies often dismissed as 'ecological'. Such designs are the only observational study design that can measure the overall effect of a vaccination programme [indirect (herd) as well as direct effects], and are in fact already an important part of the evidence base for several vaccines currently in use. Despite the great strides towards more robust observational study designs, challenges lie ahead for evaluating best practices for achieving robust unbiased results from observational studies. This is critical for evaluation of national and global vaccine programme effectiveness. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) are an increasingly utilized resource for clinical research. While their size allows for many analytical opportunities, as with most observational data there is also the potential for bias. One of the key sources of bias in EHRs is what we term informed presence--the notion that inclusion in an EHR is not random but rather indicates that the subject is ill, making people in EHRs systematically different from those not in EHRs. In this article, we use simulated and empirical data to illustrate the conditions under which such bias can arise and how conditioning on the number of health-care encounters can be one way to remove this bias. In doing so, we also show when such an approach can impart M bias, or bias from conditioning on a collider. Finally, we explore the conditions under which number of medical encounters can serve as a proxy for general health. We apply these methods to an EHR data set from a university medical center covering the years 2007-2013. [ABSTRACT FROM AUTHOR]

Studying the effect of chronic medication exposure by means of a case-crossover design may result in an upward-biased odds ratio. In this study, our aim was to assess the occurrence of this bias and to evaluate whether it is remedied by including a control group (the case-time-control design). Using Danish data resources from 1995-2012, we conducted case-crossover and case-time-control analyses for 3 medications (statins, insulin, and thyroxine) in relation to 3 outcomes (retinal detachment, wrist fracture, and ischemic stroke), all with assumed null associations. Controls were matched on age, sex, and index date, and exposure over the preceding 12 months was ascertained. For retinal detachment, the case-crossover odds ratio was 1.60 (95% confidence interval (CI): 1.42, 1.80) for statins, 1.40 (95% CI: 1.02, 1.92) for thyroxine, and 1.53 (95% CI: 1.04, 2.24) for insulin. Estimates for the retinal detachment controls were similar, leading to near-null case-time-control estimates for all 3 medication classes. For wrist fracture and stroke, the odds ratios were higher for cases than for controls, and case-time-control odds ratios were consistently above unity, thus implying significant residual bias. In case-crossover studies of medications, contamination by persistent users confers a moderate bias upward, which is partly remedied by using a control group. The optimal strategy for dealing with this problem is currently unknown. [ABSTRACT FROM AUTHOR]

Background: In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates xi are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. Methods: We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. Results: All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. Conclusion: We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion. [ABSTRACT FROM AUTHOR]

Background: Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Methods: Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r2) for each model. Results: Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Conclusions: Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors. [ABSTRACT FROM AUTHOR]

We used full-system-estimation instrumental-variables simultaneous equations modeling (IV-SEM) to examine physical activity relative to body mass index (BMI; weight (kg)/height (m)²) using 25 years of data (1985/1986 to 2010/2011) from the Coronary Artery Risk Development in Young Adults (CARDIA) Study (n = 5,115; ages 18-30 years at enrollment). Neighborhood environment and sociodemographic instruments were used to characterize physical activity, fast-food consumption, smoking, alcohol consumption, marriage, and childbearing (women) and to predict BMI using semiparametric full-information maximum likelihood estimation to control for unobserved time-invariant and time-varying residual confounding and differential measurement error through model-derived discrete random effects. Comparing robust-variance ordinary least squares, random-effects regression, fixed-effects regression, single-equation-estimation IV-SEM, and full-system-estimation IV-SEM, estimates from random- and fixed-effects models and the full-system-estimation IV-SEM were unexpectedly similar, despite the lack of control for residual confounding with the random-effects estimator. Ordinary least squares tended to overstate the significance of health behaviors in BMI, while results from single-equation-estimation IV-SEM were notably different, revealing the impact of weak instruments in standard instrumental-variable methods. Our robust findings for fixed effects (which does not require instruments but has a high cost in lost degrees of freedom) and full-system-estimation IV-SEM (vs. standard IV-SEM) demonstrate potential for a full-system-estimation IV-SEM method even with weak instruments. [ABSTRACT FROM AUTHOR]

In this issue of the Journal, Reeves et al. (Am J Epidemiol. 2016; 184(6):421 -429) present the findings of a natural experiment analyzing the association between reduced housing affordability and mental ill health. Their difference-in-difference analysis of cross-sectional, quarterly population health surveys administered before and after implementation of a policy to reduce Housing Benefit payments in the United Kingdom in April 2011 represents an important way to assess the impact of a national housing policy shift on public health. It is a well-conducted study harnessing a natural experiment and adds to the weight of evidence supporting an association between housing costs and mental health. However, quantitative bias analysis based on the reported findings suggests that a small amount of differential (by unblinded Housing Benefit status) misclassification bias in the outcome may be enough to explain the observed association. Our analysis of possible misclassification bias in the outcome used in the study highlights the need for caution when a difference-in-difference estimate is small, the population is not blinded to its postintervention exposure status, and the outcome measure is subjective and prone to differential (by unblinded exposure or treatment status) misclassification. [ABSTRACT FROM AUTHOR]

In psychiatry and psychology, relationship patterns connecting disorders and risk factors are always complex and intricate. Advanced statistical methods have been developed to overcome this issue, the most common being structural equation modelling (SEM). The main approach to SEM (CB-SEM for covariance-based SEM) has been widely used by psychiatry and psychology researchers to test whether a comprehensive theoretical model is compatible with observed data. While the validity of this approach method has been demonstrated, its application is limited in some situations, such as early-stage exploratory studies using small sample sizes. The partial least squares approach to SEM (PLS-SEM) has risen in many scientific fields as an alternative method that is especially useful when sample size restricts the use of CB-SEM. In this article, we aim to provide a comprehensive introduction to PLS-SEM intended to CB-SEM users in psychiatric and psychological fields, with an illustration using data on suicidality among prisoners. Researchers in these fields could benefit from PLS-SEM, a promising exploratory technique well adapted to studies on infrequent diseases or specific population subsets. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aim: To overcome the lack of a validated and robust Arabic instrument to measure breast cancer awareness. Background: Currently, there is no validated Arabic instrument for measuring breast cancer awareness levels. We adapted, translated and validated the Breast Cancer Awareness Measure developed by Cancer Research UK. Methods: The instrument was translated into Arabic and back ‐ translated for validation. Validation and reliability tests were conducted using purposively sampled 972 Arab women older than 20 years, living in Oman. The adapted content was validated by a panel of medical, linguistic and cultural experts, followed by cognitive interviews (n = 10), behavioural coding (n = 30) and criterion validation (n = 646). The instrument was tested for acceptability and its subscales for internal consistency. Inter ‐ rater reliability was estimated between two similar groups (n = 144 and n = 142) to test homogeneity. Results: The adapted and translated instrument had a high acceptability (98.7% completed). The validation process shaped the adaptation, and resulted in strong criterion validity (R = 0.58, P < 0.01). The instrument subscales for risk factors and warning signs had high internal consistency (Cronbach's alpha 0.856 and 0.890, respectively), with all floor and ceiling effects less than 15%. The correlation measure for inter ‐ rater reliability was 0.97 (P < 0.01). Discussion: Through the incorporation of contextual characteristics and prevalent beliefs among Arab populations, the adapted Best Cancer Awareness Measure is a robust Arabic instrument for the measurement of breast cancer awareness and early detection practices among Arab women. Limitations: The purposively selected sample may not be representative of the population. Conclusion and implications for health policy: Improvement of awareness and early detection of breast cancer can contribute towards reducing mortality from the disease. The adapted instrument has policy implications, since measurement of awareness levels is essential towards breast health promotion policies in Arab countries. [ABSTRACT FROM AUTHOR]

Influenza viruses undergo frequent antigenic changes. As a result, the viruses circulating change within and between seasons, and the composition of the influenza vaccine is updated annually. Thus, estimation of the vaccine's effectiveness is not constant across seasons. In order to provide annual estimates of the influenza vaccine's effectiveness, health departments have increasingly adopted the "test-negative design," using enhanced data from routine surveillance systems. In this design, patients presenting to participating general practitioners with influenza-like illness are swabbed for laboratory testing; those testing positive for influenza virus are defined as cases, and those testing negative form the comparison group. Data on patients' vaccination histories and confounder profiles are also collected. Vaccine effectiveness is estimated from the odds ratio comparing the odds of testing positive for influenza among vaccinated patients and unvaccinated patients, adjusting for confounders. The test-negative design is purported to reduce bias associated with confounding by health-care-seeking behavior and misclassification of cases. In this paper, we use directed acyclic graphs to characterize potential biases in studies of influenza vaccine effectiveness using the test-negative design. We show how studies using this design can avoid or minimize bias and where bias may be introduced with particular study design variations. [ABSTRACT FROM AUTHOR]

In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time-distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995-2008). [ABSTRACT FROM AUTHOR]

Traditional epidemiologic approaches allow us to compare counterfactual outcomes under 2 exposure distributions, usually 100% exposed and 100% unexposed. However, to estimate the population health effect of a proposed intervention, one may wish to compare factual outcomes under the observed exposure distribution to counterfactual outcomes under the exposure distribution produced by an intervention. Here, we used inverse probability weights to compare the 5-year mortality risk under observed antiretroviral therapy treatment plans to the 5-year mortality risk that would had been observed under an intervention in which all patients initiated therapy immediately upon entry into care among patients positive for human immunodeficiency virus in the US Centers for AIDS Research Network of Integrated Clinical Systems multisite cohort study between 1998 and 2013. Therapy-naïve patients (n = 14,700) were followed from entry into care until death, loss to follow-up, or censoring at 5 years or on December 31, 2013. The 5-year cumulative incidence of mortality was 11.65% under observed treatment plans and 10.10% under the intervention, yielding a risk difference of -1.57% (95% confidence interval: -3.08, -0.06). Comparing outcomes under the intervention with outcomes under observed treatment plans provides meaningful information about the potential consequences of new US guidelines to treat all patients with human immunodeficiency virus regardless of CD4 cell count under actual clinical conditions. [ABSTRACT FROM AUTHOR]

The exposome has been defined as the totality of exposures individuals experience over the course of their lives and how those exposures affect health. Three domains of the exposome have been identified: internal, specific external, and general external. Internal factors are those that are unique to the individual, and specific external factors include occupational exposures and lifestyle factors. The general external domain includes sociodemographic factors such as educational level and financial status. Eliciting information on the exposome is daunting and not feasible at present; the undertaking may never be fully realized. A variety of tools have been identified to measure the exposome. Biomarker measurements will be one of the major tools in exposomic studies. However, exposure data can also be obtained from other sources such as sensors, geographic information systems, and conventional tools such as survey instruments. Proof-of-concept studies are being conducted that show the promise of exposomic investigation and the integration of different kinds of data. The inherent value of exposomic data in epidemiologic studies is that they can provide greater understanding of the relationships among a broad range of chemical and other risk factors and health conditions and ultimately lead to more effective and efficient disease prevention and control. [ABSTRACT FROM AUTHOR]

In cohort studies, “immortal time” bias refers to a portion of time during which events cannot occur for a particular group of participants. Typically, immortal time bias occurs when: 1) Exposure can be initiated after follow-up of cohort members has begun; and 2) analytically, the preexposure experience is combined with that which takes place following exposure, rather than (correctly) as part of the experience of nonexposed individuals. Using the example of a cohort study of mortality in relation to receipt of cataract surgery, we sought to describe those study design and population characteristics that influence the magnitude of immortal time bias, so as to aid readers in gauging its impact on published research findings. These characteristics include the mean interval between cohort entry and when exposure criteria are met, the proportion of exposed study participants, and the length of study follow-up. [ABSTRACT FROM AUTHOR]

One of the first steps in planning a research study is the choice of study design. The available study designs are divided broadly into two types – observational and interventional. Of the various observational study designs, the descriptive design is the simplest. It allows the researcher to study and describe the distribution of one or more variables, without regard to any causal or other hypotheses. This article discusses the subtypes of descriptive study design, and their strengths and limitations. [ABSTRACT FROM AUTHOR]

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on "study designs," we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs. [ABSTRACT FROM AUTHOR]

Previous studies have compared calipers for propensity score (PS) matching, but none have considered calipers for matching on the disease risk score (DRS). We used Medicare claims data to perform 3 cohort studies of medication initiators: a study of raloxifene versus alendronate in 1 -year nonvertebral fracture risk, a study of cyclooxygenase 2 inhibitors versus nonselective nonsteroidal antiinflammatory medications in 6-month gastrointestinal bleeding, and a study of simvastatin + ezetimibe versus simvastatin alone in 6-month cardiovascular outcomes. The study periods for each cohort were 1998 through 2005,1999 through 2002, and 2004 through 2005, respectively. In each cohort, we calculated 1) a DRS, 2) a prognostic PS which included the DRS as the independent variable in a PS model, and 3) the PS for each patient. We then nearest-neighbor matched on each score in a variable ratio and a fixed ratio within 8 calipers based on the standard deviation of the logit and the natural score scale. When variable ratio matching on the DRS, a caliper of 0.05 on the natural scale performed poorly when the outcome was rare. The prognostic PS did not appear to offer any consistent practical benefits over matching on the DRS directly. In general, logit-based calipers or calipers smaller than 0.05 on the natural scale performed well when DRS matching in all examples. [ABSTRACT FROM AUTHOR]

The rising incidence of infertility among couples of childbearing age has been a global concern. The ovulatory and tubal factors are the common causes of female infertility, including the inflammation, adhesion and obstruction of oviduct. Pelvic inflammatory disease is the most common cause for oviduct infertility, which affected by Chlamydia trachomatis, Ureaplasma urealyticum, Gonococcus or tubercle bacillus. Besides, appendicitis, induced abortion, abdominopelvic surgery, endometriosis and congenital dysplasia of fallopian tube are also the pathogenesis of oviduct infertility. The prevalence of tubal infertility brings not only the great economic burden to the society, but also the psychological pressure to the patients. This systematic review on the epidemiology and etiology of tubal infertility may help doctors to optimize the prevention, diagnosis and treatment of tubal infertility. [ABSTRACT FROM AUTHOR]

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 um or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87,18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94,1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m³-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias. [ABSTRACT FROM AUTHOR]

Background: Preventative inhaled treatments in cystic fibrosis will only be effective in maintaining lung health if used appropriately. An accurate adherence index should therefore reflect treatment effectiveness, but the standard method of reporting adherence, that is, as a percentage of the agreed regimen between clinicians and people with cystic fibrosis, does not account for the appropriateness of the treatment regimen. We describe two different indices of inhaled therapy adherence for adults with cystic fibrosis which take into account effectiveness, that is, "simple" and "sophisticated" normative adherence. Methods to calculate normative adherence: Denominator adjustment involves fixing a minimum appropriate value based on the recommended therapy given a person's characteristics. For simple normative adherence, the denominator is determined by the person's Pseudomonas status. For sophisticated normative adherence, the denominator is determined by the person's Pseudomonas status and history of pulmonary exacerbations over the previous year. Numerator adjustment involves capping the daily maximum inhaled therapy use at 100% so that medication overuse does not artificially inflate the adherence level. Three illustrative cases: Case A is an example of inhaled therapy under prescription based on Pseudomonas status resulting in lower simple normative adherence compared to unadjusted adherence. Case B is an example of inhaled therapy under-prescription based on previous exacerbation history resulting in lower sophisticated normative adherence compared to unadjusted adherence and simple normative adherence. Case C is an example of nebulizer overuse exaggerating the magnitude of unadjusted adherence. Conclusion: Different methods of reporting adherence can result in different magnitudes of adherence. We have proposed two methods of standardizing the calculation of adherence which should better reflect treatment effectiveness. The value of these indices can be tested empirically in clinical trials in which there is careful definition of treatment regimens related to key patient characteristics, alongside accurate measurement of health outcomes. [ABSTRACT FROM AUTHOR]

The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, we show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, we conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. Our results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. We also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Our estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article we develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Our design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method. [ABSTRACT FROM AUTHOR]

Straight metalworking fluids have been linked to cardiovascular mortality in analyses using binary exposure metrics, accounting for healthy worker survivor bias by using g-estimation of accelerated failure time models. A cohort of 38,666 Michigan autoworkers was followed (1941-1994) for mortality from all causes and ischemic heart disease. The structural model chosen here, using continuous exposure, assumes that increasing exposure from 0 to 1 mg/m³ in any single year would decrease survival time by a fixed amount. Under that assumption, banning the fluids would have saved an estimated total of 8,468 (slope-based 95% confidence interval: 2,262,28,563) person-years of life in this cohort. On average, 3.04 (slope-based 95% confidence interval: 0.02, 25.98) years of life could have been saved for each exposed worker who died from ischemic heart disease. Estimates were sensitive to both model specification for predicting exposure (multinomial or logistic regression) and characterization of exposure as binary or continuous in the structural model. Our results provide evidence supporting the hypothesis of a detrimental relationship between straight metalworking fluids and mortality, particularly from ischemic heart disease, as well as an instructive example of the challenges in obtaining and interpreting results from accelerated failure time models using a continuous exposure in the presence of competing risks. [ABSTRACT FROM AUTHOR]

Conventional measures of causality (which compare risks between exposed and unexposed individuals) do not factor in the population-scale dynamics of infectious disease transmission. We used mathematical models of 2 childhood infections (respiratory syncytial virus and rotavirus) to illustrate this problem. These models incorporated 3 causal pathways whereby malnutrition could act to increase the incidence of severe infection: increasing the proportion of infected children who develop severe infection, increasing the children's susceptibility to infection, and increasing infectiousness. For risk factors that increased the proportion of infected children who developed severe infection, the population attributable fraction (PAF) calculated conventionally was the same as the PAF calculated directly from the models. However, for risk factors that increased transmission (by either increasing susceptibility to infection or increasing infectiousness), the PAF calculated directly from the models was much larger than that predicted by the conventional PAF calculation. The models also showed that even when conventional studies find no association between a risk factor and an outcome, risk factors that increase transmission can still have a large impact on disease burden. For a complete picture of infectious disease causality, transmission effects must be incorporated into causal models. [ABSTRACT FROM AUTHOR]

Study Design: Methodological review of randomized controlled trials (RCTs).Objective: To assess the quality of reporting of rehabilitation interventions for mechanical low back pain (LBP) in published RCTs.Summary Of Background Data: Reporting of interventions in RCTs often focused on the outcome value and failed to describe interventions adequately.Methods: We systematically searched for all RCTs in Cochrane systematic reviews on LBP published in the Cochrane Database of Systematic Reviews until December 2013. The description of rehabilitation interventions of each RCT was evaluated independently by 2 of the investigators, using an ad hoc checklist of 7 items. The primary outcome was the number of items reported in sufficient details to be replicable in a new RCT or in everyday practice.Results: We found 11 systematic reviews, including 220 eligible RCTs, on LBP. Of those, 185 RCTs were included. The median publication year was 1998 (I-III quartiles, 1990 to 2004). The most reported items were the characteristics of participants (91.3%; 95% confidence interval [CI], 87.3-95.4), the intervention providers (81.1%; 95% CI, 75.4-86.7), and the intervention schedule (69.7%; 95% CI, 63-76). Based on the description of the intervention, less than one fifth would be replicable clinically. The proportion of trials providing all essential information about the participants and interventions increased from 14% (n = 7) in 1971 to 1980 to 20% (n = 75) in 2001 to 2010.Conclusion: Despite the remarkable amount of energy spent producing RCTs in LBP rehabilitation, the majority of RCTs failed to report sufficient information that would allow the intervention to be replicated in clinical practice. Improving the quality of intervention description is urgently needed to better transfer research into rehabilitation practices.Level Of Evidence: 1. [ABSTRACT FROM AUTHOR]

A century's worth of efforts to better understand the epidemiology of tuberculosis (TB) and to develop new vaccines, drugs, preventive interventions, and case-finding approaches have provided important insights and helped to advance the field of epidemiology as a whole. Wade Hampton Frost developed methods for cohort analysis that formed the early basis for adjustment of confounding variables. The streptomycin trial in the United Kingdom in the 1940s introduced random allocation for participants to either the treatment or control group, ensuring blinded treatment assignment and comparable treatment groups, which is now a key element in randomized clinical trials. Research into the bacille Calmette-Guérin vaccine demonstrated the importance of comparative analyses, potential difficulties in generalizability to populations not under study, and the role of meta-analysis for discrepant data--approaches now strongly recommended prior to implementing any novel public health intervention. George Comstock's work on preventive therapy for TB demonstrated the use of epidemiologic methods to evaluate interventions on a population level. Finally, studies from the Consortium to Respond Effectively to the AIDS/TB Epidemic focused on the evaluation of real-world effectiveness and of targeting of high-risk subpopulations. In this article, we discuss how TB research in each of these domains has helped to advance epidemiologic thinking and methodology over the past 100 years. [ABSTRACT FROM AUTHOR]

In this article, I provide a perspective on the tobacco epidemic and epidemiology, describing the impact of the tobacco-caused disease epidemic on the field of epidemiology. Although there is an enormous body of epidemiologic evidence on the associations of smoking with health, little systematic attention has been given to how decades of research have affected epidemiology and its practice. I address the many advances that resulted from epidemiologic research on smoking and health, such as demonstration of the utility of observational designs and important parameters (the odds ratio and the population attributable risk), guidelines for causal inference, and systematic review approaches. I also cover unintended and adverse consequences for the field, including the strategy of doubt creation and the recruitment of epidemiologists by the tobacco industry to serve its mission. The paradigm of evidence-based action for addressing noncommunicable diseases began with the need to address the epidemic of tobacco-caused disease, an imperative for action documented by epidemiologic research. [ABSTRACT FROM AUTHOR]

Objective: Despite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction.Data Sources: We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin.Study Design: A total of 643 variables were identified at baseline from prior claims and linked Census data. In addition, we identified three postbaseline predictors, indicators of adherence to statins during each of the first 3 months of follow-up. We estimated 10 models predicting subsequent adherence, using logistic regression and boosted logistic regression, a nonparametric data-mining technique. Models were also estimated within strata defined by the index days supply.Results: In 77,703 statin initiators, prediction using baseline variables only was poor with maximum cross-validated C-statistics of 0.606 and 0.577 among patients with index supply ≤30 days and >30 days, respectively. Using only indicators of initial statin adherence improved prediction accuracy substantially among patients with shorter initial dispensings (C = 0.827/0.518), and, when combined with investigator-specified variables, prediction accuracy was further improved (C = 0.842/0.596).Conclusions: Observed adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short. [ABSTRACT FROM AUTHOR]

Because public health agencies usually monitor health outcomes over time for surveillance, program evaluation, and policy decisions, a correct health outcome trend analysis is vital. If the analysis is done incorrectly and/or results are misinterpreted, a faulty trend analysis could jeopardize key aspects of public health initiatives such as program planning, implementations, policy development, and clinical decision making. It is essential then that accurate health outcome trend analysis be implemented in any data-driven decision-making process. Unfortunately, there continues to be common statistical mistakes in prevalence trend analysis. In this article, using recently published results from the Pediatric Nutrition Surveillance System, we will show the effect that an incorrect trend analysis and subsequent interpretation of results can have. We will also propose more appropriate statistical processes, such as the log-binomial model, for these situations. [ABSTRACT FROM AUTHOR]

Background: Cross-sectional studies of occupational noise and cardiovascular effects show an association between noise and hypertension but for coronary heart disease or other cardiovascular diseases (CVDs) the evidence is not convincing. Aims: To assess possible associations between occupational noise exposure and the risk for cardiovascular effects in follow-up studies published after 1999. Methods: We performed a systematic critical literature review of original articles from key literature databases of associations between workplace noise and health. The studies were identified by search in Ovid Medline, Ovid Embase, Web of Science, Scopus and ProQuest Health and Safety Sciences Abstracts. We selected prospective studies of adequate quality with a measure of association between occupational noise exposure and cardiovascular health for the meta-analysis. Results: Twelve papers, all prospective and mostly with high quality but with methodological shortcomings in exposure assessment, were included in the review and meta-analysis. Exposure to noise at work was consistently positively associated with hypertension [Hazard ratio (HR) = 1.68; 95% confidence interval (CI) 1.10-2.57] and CVD [relative risk (RR) = 1.34; 95% CI 1.15-1.56]. In addition, we found a trivial effect of noise exposure on CVD mortality (HR = 1.12; 95% CI 1.02-1.24). Conclusions: Occupational noise exposure is strongly associated with hypertension. For other cardiovascular effects, this meta-analysis suggests a weak association, but the evidence is limited. More longitudinal studies on the effects of occupational noise on the cardiovascular system are warranted. [ABSTRACT FROM AUTHOR]

Background: Meta-analysis techniques were traditionally used to assess the effectiveness and safety of a treatment through direct comparison with a single comparator. More recently, network meta-analyses allowed the comparison of multiple interventions and the incorporation of clinical evidence of direct and indirect comparisons. We consider it relevant to address the main methodological considerations on this theme because of its topicality. Objectives: To demonstrate how to perform and interpret a network meta-analysis for indirect and mixed comparisons. Main topics under analysis: We present some key concepts of a network meta-analysis for indirect and mixed comparisons and the assumptions for its application. We point out some of the methodological challenges and opportunities for application of this statistical technique resulting from the theoretical and methodological analysis of articles published in the last 5 years. Conclusions: The use of indirect and mixed comparisons to increase the statistical power is an essential contribution to the current meta-analysis techniques, allowing researchers to improve the process of synthesis and health professionals to apply the best available evidence. [ABSTRACT FROM AUTHOR]