Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal ...effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population before the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias owing to restricting to one or more level(s) of a collider (or a descendant of a collider) and type 2 selection bias owing to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types-which can co-occur-we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then, we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of "M-bias" to demonstrate the advantage of classifying selection bias into these two types.
Selection bias due to loss to follow up represents a threat to the internal validity of estimates derived from cohort studies. Over the past 15 years, stratification-based techniques as well as ...methods such as inverse probability-of-censoring weighted estimation have been more prominently discussed and offered as a means to correct for selection bias. However, unlike correcting for confounding bias using inverse weighting, uptake of inverse probability-of-censoring weighted estimation as well as competing methods has been limited in the applied epidemiologic literature. To motivate greater use of inverse probability-of-censoring weighted estimation and competing methods, we use causal diagrams to describe the sources of selection bias in cohort studies employing a time-to-event framework when the quantity of interest is an absolute measure (e.g., absolute risk, survival function) or relative effect measure (e.g., risk difference, risk ratio). We highlight that whether a given estimate obtained from standard methods is potentially subject to selection bias depends on the causal diagram and the measure. We first broadly describe inverse probability-of-censoring weighted estimation and then give a simple example to demonstrate in detail how inverse probability-of-censoring weighted estimation mitigates selection bias and describe challenges to estimation. We then modify complex, real-world data from the University of North Carolina Center for AIDS Research HIV clinical cohort study and estimate the absolute and relative change in the occurrence of death with and without inverse probability-of-censoring weighted correction using the modified University of North Carolina data. We provide SAS code to aid with implementation of inverse probability-of-censoring weighted techniques.
Increasing evidence suggests that psychological well-being (PWB) is associated with lower disease and mortality risk, and may be enhanced with relatively low-cost interventions. Yet, dissemination of ...these interventions remains limited, in part because insufficient attention has been paid to distinct PWB dimensions, which may impact physical health outcomes differently.
This essay first reviews the empirical evidence regarding differential relationships between all-cause mortality and multiple dimensions of PWB (e.g., life purpose, mastery, positive affect, life satisfaction, optimism). Then, individual-level positive psychology interventions aimed at increasing PWB and tested in randomized-controlled trials are reviewed as these allow for easy implementation and potentially broad outreach to improve population well-being, in concert with efforts targeting other established social determinants of health.
Several PWB dimensions relate to mortality, with varying strength of evidence. Many of positive psychology trials indicate small-to-moderate improvements in PWB; rigorous institution-level interventions are comparatively few, but preliminary results suggest benefits as well. Examples of existing health policies geared towards the improvement of population well-being are also presented. Future avenues of well-being epidemiological and intervention research, as well as policy implications, are discussed.
Although research in the fields of behavioral and psychosomatic medicine, as well as health psychology have substantially contributed to the science of PWB, this body of work has been somewhat overlooked by the public health community. Yet, the growing interest in documenting well-being, in addition to examining its determinants and consequences at a population level may provoke a shift in perspective. To cultivate optimal well-being-mental, physical, social, and spiritual-consideration of a broader set of well-being measures, rigorous studies, and interventions that can be disseminated is critically needed.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United ...States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g., gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities.
Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have ...limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.
This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.
Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.
This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Early-life exposure to bisphenol A (BPA) may increase childhood obesity risk, but few prospective epidemiological studies have investigated this relationship.
We sought to determine whether ...early-life exposure to BPA was associated with increased body mass index (BMI) at 2-5 years of age in 297 mother-child pairs from Cincinnati, Ohio (HOME Study).
Urinary BPA concentrations were measured in samples collected from pregnant women during the second and third trimesters and their children at 1 and 2 years of age. BMI z-scores were calculated from weight/height measures conducted annually from 2 through 5 years of age. We used linear mixed models to estimate BMI differences or trajectories with increasing creatinine-normalized BPA concentrations.
After confounder adjustment, each 10-fold increase in prenatal (β = -0.1; 95% CI: -0.5, 0.3) or early-childhood (β = -0.2; 95% CI: -0.6, 0.1) BPA concentrations was associated with a modest and nonsignificant reduction in child BMI. These inverse associations were suggestively stronger in girls than in boys prenatal effect measure modification (EMM) p-value = 0.30, early-childhood EMM p-value = 0.05, but sex-specific associations were imprecise. Children in the highest early-childhood BPA tercile had lower BMI at 2 years (difference = -0.3; 95% CI: -0.6, 0.0) and larger increases in their BMI slope from 2 through 5 years (BMI increase per year = 0.12; 95% CI: 0.07, 0.18) than children in the lowest tercile (BMI increase per year = 0.07; 95% CI: 0.01, 0.13). All associations were attenuated without creatinine normalization.
Prenatal and early-childhood BPA exposures were not associated with increased BMI at 2-5 years of age, but higher early-childhood BPA exposures were associated with accelerated growth during this period.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Abstract
Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical ...set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g., mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies.
•Case-cohort study included a randomly selected subcohort and 809 AMI cases.•Participants had relatively low urinary cadmium compared to prior studies.•We found little evidence that cadmium at ...relatively low-levels is associated with AMI.•Results were not materially different across strata defined by sex.•Similar results using creatinine or osmolality to account for urine dilution.
Cadmium exposure has been associated with cardiovascular disease. Cigarette smoking is a key source of cadmium exposure and thus a potential confounder in observational studies of environmental cadmium and cardiovascular disease that include tobacco smokers. We leveraged up to 20 years of follow-up in the Danish Diet, Cancer and Health cohort to test the hypothesis that cadmium exposure is associated with acute myocardial infarction (AMI) among people who never smoked. Between 1993 and 1997, 19,394 never-smoking participants (ages 50–64 years) were enrolled and provided a urine sample. From this sample, we randomly selected a subcohort of 600 males and 600 females. We identified 809 AMI cases occurring between baseline and the end of 2015 using the Danish National Patient Registry. We quantified cadmium, creatinine, and osmolality in baseline urine samples. Using an unweighted case-cohort approach, we estimated adjusted hazard ratios (aHR) for AMI in Cox proportional hazards models with age as the time axis. Participants had relatively low concentrations of urinary cadmium, as expected for never smokers (median = 0.20; 25th, 75th = 0.13, 0.32 μg cadmium/g creatinine). We did not find strong evidence to support an association between higher urinary cadmium and AMI when comparing the highest versus lowest quartile (aHR = 1.16; 95% CI: 0.86 – 1.56) and per IQR increment in cadmium concentration (aHR = 1.02; 95% CI: 0.93 – 1.12). Results were not materially different across strata defined by sex. Results were generally similar using creatinine or osmolality to account for differences in urine dilution. While cadmium exposure has been identified as a risk factor for cardiovascular disease, we did not find strong evidence that urinary cadmium at relatively low-levels is associated with AMI among people who have never smoked.
The impact of survival-related selection bias has not always been discussed in relevant studies of racial health disparities. Moreover, the analytic approaches most frequently employed in the ...epidemiologic literature to minimize selection bias are difficult to implement appropriately in racial disparities research. This difficulty stems from the fact that frequently employed analytic techniques require that common causes of survival and the outcome are accurately measured. Unfortunately, such common causes are often unmeasured or poorly measured in racial health disparities studies. In the absence of accurate measures of the aforementioned common causes, redefining the target population or changing the study design represents a useful approach for reducing the extent of survival-related selection bias. To help researchers recognize and minimize survival-related selection bias in racial health disparities studies, we illustrate the aforementioned selection bias and how redefining the target population or changing the study design can be useful.
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