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Objectives To explore how general practitioners operate the sickness certification system, their views on the system, and suggestions for change.

Introduction: Research has documented growing availability and use of e-cigarettes in the United States over the last decade. Methods: We conducted a national panel survey of current adult cigarette smokers to assess attitudes, beliefs, and behaviors relating to e-cigarette use in the United States (N = 2,254). Results: Among current cigarette smokers, 20.4% reported current use of e-cigarettes on some days and 3.7% reported daily use. Reported reasons for e-cigarette use included: quit smoking (58.4%), reduce smoking (57.9%), and reduce health risks (51.9%). No significant differences in sociodemographic characteristics between e-cigarette users and nonusers were observed. Prior quit attempts were reported more frequently among e-cigarette users (82.8%) than nonusers (74.0%). Intention to quit was reported more frequently among e-cigarette users (64.7%) than nonusers (46.8%). Smokers intending to quit were more likely to be e-cigarette users than those not intending to quit (odds ratio [OR] = 1.90, CI =1.36-2.65). Those who used e-cigarettes to try to quit smoking (OR = 2.25, CI = 1.25-4.05), reduce stress (OR = 3.66, CI = 1.11-12.09), or because they cost less (OR = 3.42, CI = 1.64-7.13) were more likely to report decreases in cigarette smoking than those who did not indicate these reasons. Smokers who reported using e-cigarettes to quit smoking (OR = 16.25, or reduce stress (OR = 4.30, CI = 1.32-14.09) were significantly more likely to report an intention to quit than those who did not indicate those reasons for using e-cigarettes. Conclusions: Nearly a quarter of smokers in our study reported e-cigarettes use, primarily motivated by intentions to quit or reduce smoking. These findings identify a clinical and public health opportunity to re-engage smokers in cessation efforts.

This study suggests that trained and supervised nurses can effectively deliver CBT for insomnia in routine general medical practice. Treatment response to small-group service delivery was encouraging, although effect sizes were smaller than those obtained in efficacy studies. Further research is required to consider the possibility that CBT could become the treatment of first choice for persistent insomnia in primary healthcare.

In a series of methodological papers, we have described the Rochester Epidemiology Project (REP) medical recordslinkage system as it has existed for more than 50 years in Olmsted County, Minnesota. [1][2][3][4] Further details about the major events and protagonists of the history of the original REP are available elsewhere. 1 Starting in 2010, we have expanded the population captured by the REP from a single county in south-eastern Minnesota to a geographical region including 27 counties in southern Minnesota and western Wisconsin. In this paper, we provide a profile of the expanded medical records-linkage system, which we name the Expanded-REP (E-REP) to distinguish it from the original REP. Because the data became available for electronic linkage and storage starting in 2010, we can consider 2010 the birth year for the E-REP. General descriptionThe E-REP was established to provide longitudinal medical data for a population residing in a well-defined geographical region. The E-REP captures a large percentage of the persons who have resided in a 27-county region of southern Minnesota and western Wisconsin at some time from 1 January 2010 to the present, regardless of age, sex, ethnicity and disease status. Depending on the needs for a specific study, the region can also be partitioned into smaller segments. For example, some studies have targeted a sevencounty region or an 11-county region because these regions have a higher percentage of population capture; therefore, the non-participation percentage is lower. [5][6][7] The electronic indexes of the E-REP include not only demographic information, diagnostic and procedure codes, health services utilization data and outpatient drug prescriptions, but also results of laboratory tests and information about smoking, height, weight and body mass index. Table 1 shows a list of data that are currently included in the electronic indexes and their definitions. Methods for linkage across institutionsThe E-REP includes medical record data from multiple health care institutions, and these institutions currently use