Cartesian product files have recently been shown to exhibit attractive properties for partial match queries. This paper considers the file allocation problem for Cartesian product files, which can be stated as follows: Given a k-attribute Cartesian product file and an m-disk system, allocate buckets among the m disks in such a way that, for all possible partial match queries, the concurrency of disk accesses is maximis ed. The Risk Modulo (DM) allocation method is described first, and it is shown to be strict optimal under many conditions commonly occurring in practice, including all possible partial match queries when the number of disks is 2 or 3. It is also shown that although it has good performance, the DM allocation method is not strict optimal for all possible partial match queries when the number of disks is greater than 3. The General Disk Modulo (GDM) allocation method is then described, and a sufficient but not necessary condition for strict optimal&y of the GDM method for all partial match queries and any number of disks is then derived. Simulation studies comparing the DM and random allocation methods in terms of the average number of disk accesses, in response to various classes of partial match queries, show the former to be significantly more effective even when the number of disks is greater than 3, that is, even in cases where the DM method is not strict optimal. The results that have been derived formally and shown by simulation can be used for more effective design of optimal file systems for partial match queries. When considering multiple-disk systems with independent access paths, it is important to ensure that similar records are clustered into the same or similar buckets, while similar buckets should be dispersed uniformly among the disks.

Background: We conducted a discrete choice experiment (DCE) among young adult cigarette smokers in the period July–August 2018 to examine their preference for cigarillos in response to various packaging-related attributes, including flavor, flavor description, quality descriptors, pack size, and prices. Methods: A convenience sample of 566 US young adult cigarette smokers aged 18–34, among whom 296 were current little cigar and cigarillo (LCC) smokers, were recruited using Facebook ads and invited to participate in an online (Qualtrics) tobacco survey containing DCE and tobacco use questions. In the experiment, participants chose among two cigarillo products or “neither” (opt-out). Results: We analyzed preferences for LCCs using multinomial, nested, random parameter logit models. Results showed that young adult cigarette smokers preferred grape over menthol, tobacco/regular, and wine flavors; “color only” and “color and text” flavor depictions over text only; “smooth” and “sweet” quality descriptors over “satisfying”; and larger pack sizes and lower prices. Conclusions: Regulating packaging-related features will impact LCC choices among US young adult smokers. FDA regulation over these packaging-related features may impact LCC use among young adult smokers.

As the price/performance ratio of computers keeps going down, their use is becoming feasible for more and more medical applications. An important factor contributing to the success of such applictions is the care and time spent in the analysis, design, development, implementation and operation phases. Although much literature is available on the actual programming aspects such as structured programming or top-down design techniques for large computer applications, little is said about the human and other factors that should be taken into account to ensure an end product that not only functions correctly but is readily acceptable to those who must use it. The purpose of this paper, therefore, is to present some of the problems that are more frequently encountered in medical computer applications and to focus attention to those items that should be carefully considered as the application evolves from the analysis and design phases to the operational phase. Some suggestions of overcoming these problems are also presented.

IntroductionSocial media ad campaigns can be an efficient, cost-effective way to recruit for studies online, especially as the onset of the COVID-19 pandemic limited in-person recruitment. Early Check, a large ongoing study offering testing for a panel of conditions for all newborns in North Carolina, uses social media ad campaigns, along with direct mail, email, print materials in health care settings, and messages through patient portals to contact pregnant women and mothers with eligible newborns. All materials refer women to the online Early Check portal for consent and enrollment in the study.MethodsTo evaluate social media options for outreach and recruitment, we ran two paid ad campaigns on Pinterest in May and July 2021 and compared performance to simultaneous Facebook and Instagram campaigns.ResultsFacebook and Instagram cost $136.53 per sign-up in May and July. Our first Pinterest campaign in May resulted in 206,416 impressions, 529 link clicks, and a cost per sign-up of $536.56. After adjusting the campaign to incorporate lessons learned about the platform, the second Pinterest campaign in July resulted in 225,286 impressions, 621 link clicks, and a cost per sign-up of $216.55.DiscussionOthers looking to implement social media ad campaigns for public health recruitment should note that ad costs have increased since 2020. However, social media ad campaigns on Facebook, Instagram, and Pinterest remain a cost-effective and convenient way to recruit participants for studies, especially when in-person efforts are not feasible. Ad campaign strategy should also be tailored to the specific platform. Facebook and Instagram ads should be run together in the same campaign to optimize the budget across both platforms and should run using an on-off schedule. Pinterest campaigns should run for a longer period to optimize continually for sign-ups over time.