The current emphasis on comparative effectiveness research will provide practicing physicians with increasing volumes of observational evidence about preventive care. However, numerous highly publicized observational studies of the effect of prevention on health outcomes have reported exaggerated relationships that were later contradicted by randomized controlled trials. A growing body of research has identified sources of bias in observational studies that are related to patient behaviors or underlying patient characteristics, known as the healthy user effect, the healthy adherer effect, confounding by functional status or cognitive impairment, and confounding by selective prescribing. In this manuscript we briefly review observational studies of prevention that have appeared to reach incorrect conclusions. We then describe potential sources of bias in these studies and discuss study designs, analytical methods, and sensitivity analyses that may mitigate bias or increase confidence in the results reported. More careful consideration of these sources of bias and study designs by providers can enhance evidence-based decision-making. P racticing clinicians face a substantial challenge when attempting to interpret data from observational studies that report the effects of prevention on patient health outcomes. Numerous high-profile descriptive studies of preventive screening tests, behaviors, and treatments have reported dramatically reduced mortality or improved health outcomes. However, many of these findings were later thrown into question when randomized controlled trials (RCTs) indicated contradictory results. In some cases, the flawed observational studies were the source of evidence for broad practice recommendations.1 While it would be a mistake to ignore all evidence from observational studies-there are many questions that will never be answered by RCTs-clinicians must be careful when interpreting observational studies demonstrating what seem to be surprisingly large beneficial effects of preventive therapy. With the investment of over $1 billion in comparative effectiveness research, clinicians will be faced with increasing volumes of complex results. Proper interpretation will require familiarity with a host of sources of bias in observational research. Bias results when features of a study's design lead to estimates that do not accurately reflect the relationship between the study variables. In this review, we explore a specific subset of these sources of bias-confounding in observational studies resulting from patient-level tendencies to engage in healthy behaviors or physician's perceptions of the health of patients. A recent body of research has emerged examining these sources of bias, and their effect on the interpretation of observational research findings. In this paper, we provide a brief review of observational studies that have appeared to reach incorrect conclusions due to healthy user and other related types of bias. We describe the sources of bias in these studies and discuss study designs, ...

Patients who adhere to preventive therapies may be more likely to engage in a broad spectrum of behaviors consistent with a healthy lifestyle. Because many of these behaviors cannot be measured easily, observational studies of outcomes associated with the long-term use of preventive therapies are subject to the so-called "healthy user bias." To better understand this effect, the authors examined the association between adherence to statin therapy and the use of preventive health services in a Pennsylvania cohort of 20,783 new users of statins between 1996 and 2004. After adjustment for age, gender, and various comorbid conditions, patients who filled two or more prescriptions for a statin during a 1-year ascertainment period were more likely than patients who filled only one prescription to receive prostate-specific antigen tests (hazard ratio (HR)=1.57, 95% confidence interval (CI): 1.17, 2.19), fecal occult blood tests (HR=1.31, 95% CI: 1.12, 1.53), screening mammograms (HR=1.22, 95% CI: 1.09, 1.38), influenza vaccinations (HR=1.21, 95% CI: 1.12, 1.31), and pneumococcal vaccinations (HR=1.46, 95% CI: 1.17, 1.83) during follow-up. These results suggest that patients who adhere to chronic therapies are more likely to seek out preventive health services, such as screening tests and vaccinations. Further work is needed to identify study design and analysis methods that can be used to minimize the healthy user bias in studies of preventive therapies.

Background-The goal of restricting study populations is to make patients more homogeneous regarding potential confounding factors and treatment effects and thereby achieve less biased effect estimates.

A half million Americans have ESRD, which puts them at high risk for cardiovascular disease and poor outcomes. Little is known about the epidemiology of atrial fibrillation among patients with ESRD. We analyzed data from annual cohorts (1992 to 2006) of prevalent hemodialysis patients from the United States Renal Data System. In each cohort, we searched 1 year of medical claims for relevant diagnosis codes to determine the prevalence of atrial fibrillation. Among 2.5 million patient observations, 7.7% had atrial fibrillation, with the prevalence increasing 3-fold from 3.5% (1992) to 10.7% (2006). The number of affected patients increased from 3620 to 23,893 (6.6-fold) during this period. Older age, male gender, and several comorbid conditions were associated with increased risk for atrial fibrillation. Compared with otherwise similar Caucasians, the prevalence of atrial fibrillation rates was substantially lower for blacks, Asians, and Native Americans. One-year mortality was twice as high among hemodialysis patients with atrial fibrillation compared with those without (39% versus 19%), and this increased risk was constant during the 15 years of the study. In conclusion, the prevalence of diagnosed atrial fibrillation among patients receiving hemodialysis in the United States is increasing, varies by race, and remains associated with substantially increased mortality. Identifying potentially modifiable risk factors for incident atrial fibrillation requires further investigation.

Background-Bias in studies of preventive medications can occur when healthier patients are more likely to initiate and adhere to therapy than less healthy patients. We sought evidence of this bias by examining associations between statin exposure and various outcomes that should not be causally affected by statin exposure, such as workplace and motor vehicle accidents. Methods and Results-We conducted a prospective cohort study of statin patients using data from British Columbia, Canada, a multiethnic society with a population of 4.3 million people. Study subjects were 141 086 patients who initiated statins for primary prevention. We examined the association between adherence and multiple outcomes such as accidents and screening procedures using multivariable-adjusted Cox proportional hazards models. The study population was 49% female and had an average age of 61 years. The results from our multivariable-adjusted models showed that more adherent patients were less likely to have accidents than less adherent patients. This effect was greatest for motor vehicle accidents (hazard ratio, 0.75; 95% confidence interval, 0.72 to 0.79) and workplace accidents (hazard ratio, 0.77; 95% confidence interval, 0.74 to 0.81). More adherent patients had a greater likelihood of using screening services (hazard ratio, 1.17; 95% confidence interval, 1.15 to 1.20) and a lower likelihood of developing other diseases likely to be unrelated to a biological affect of a statin (hazard ratio, 0.87; 95% confidence interval, 0.86 to 0.89). Conclusions-Our study contributes compelling evidence that patients who adhere to statins are systematically more health seeking than comparable patients who do not remain adherent. Caution is warranted when interpreting analyses that attribute surprising protective effects to preventive medications.

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This exploratory analysis suggests that the use of gabapentin, lamotrigine, oxcarbazepine, and tiagabine, compared with the use of topiramate, may be associated with an increased risk of suicidal acts or violent deaths.

Purpose Estimating drug effectiveness and safety among older adults in population-based studies using administrative healthcare claims can be hampered by unmeasured confounding due to frailty. A claims-based algorithm that identifies patients likely to be dependent, a proxy for frailty, may improve confounding control. Our objective was to develop an algorithm to predict dependency in activities of daily living (ADL) in a sample of Medicare beneficiaries. Methods Community-dwelling respondents to the 2006 Medicare Current Beneficiary Survey, >65 years old, with Medicare Part A, B, home health, and hospice claims were included. ADL dependency was defined as needing help with bathing, eating, walking, dressing, toileting, or transferring. Potential predictors were demographics, ICD-9 diagnosis/procedure and durable medical equipment codes for frailty-associated conditions. Multivariable logistic regression was to predict ADL dependency. Cox models estimated hazard ratios for death as a function of observed and predicted ADL dependency. Results Of 6391 respondents, 57% were female, 88% white, and 38% were ≥80. The prevalence of ADL dependency was 9.5%. Strong predictors of ADL dependency were charges for a home hospital bed (OR=5.44, 95% CI=3.28–9.03) and wheelchair (OR=3.91, 95% CI=2.78–5.51). The c-statistic of the final model was 0.845. Model-predicted ADL dependency of 20% or greater was associated with a hazard ratio for death of 3.19 (95% CI: 2.78, 3.68). Conclusions An algorithm for predicting ADL dependency using healthcare claims was developed to measure some aspects of frailty. Accounting for variation in frailty among older adults could lead to more valid conclusions about treatment use, safety, and effectiveness.