Many studies have reported that antibiotic use may be associated with increased risk of childhood asthma. Respiratory tract infections in small children may be difficult to distinguish from early symptoms of asthma, and studies may have been confounded by "protopathic" bias, where antibiotics are used to treat early symptoms of asthma. These analyses of a cohort including 1,401 US children assess the association between antibiotic use within the first 6 months of life and asthma and allergy at 6 years of age between 2003 and 2007. Antibiotic exposure was associated with increased risk of asthma (adjusted odds ratio = 1.52, 95% confidence interval (CI): 1.07, 2.16). The odds ratio if asthma was first diagnosed after 3 years of age was 1.66 (95% CI: 0.99, 2.79) and, in children with no history of lower respiratory infection in the first year of life, the odds ratio was 1.66 (95% CI: 1.12, 3.46). The adverse effect of antibiotics was particularly strong in children with no family history of asthma (odds ratio = 1.89, 95% CI: 1.00, 3.58) (P(interaction) = 0.03). The odds ratio for a positive allergy blood or skin test was 1.59 (95% CI: 1.10, 2.28). The results show that early antibiotic use was associated with asthma and allergy at 6 years of age, and that protopathic bias was unlikely to account for the main findings.

Antibiotics seem to slightly increase the risk of childhood asthma. Reverse causality and protopathic bias seem to be possible confounders for this relationship.

Pharmacoepidemiologic and pharmacoeconomic analysis of health care databases has become a vital source of evidence to support health care decision making and efficient management of health care organizations. However, decision makers often consider studies done in nonrandomized health care databases more difficult to review than randomized trials because many design choices need to be considered. This is perceived as an important barrier to decision making about the effectiveness and safety of medical products. Design flaws in longitudinal database studies are avoidable but can be unintentionally obscured in the convoluted prose of methods sections, which often lack specificity. We propose a simple framework of graphical representation that visualizes study design implementations in a comprehensive, unambiguous, and intuitive way; contains a level of detail that enables reproduction of key study design variables; and uses standardized structure and terminology to simplify review and communication to a broad audience of decision makers. Visualization of design details will make database studies more reproducible, quicker to review, and easier to communicate to a broad audience of decision makers.

No abstract