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ObjectiveCancer burden can be reduced when the population's knowledge of cancer prevention and control measures is increased. However, current epidemiological research investigating cancer prevention and control knowledge in China is limited. This study aimed to examine the core knowledge levels of cancer prevention and control measures as well as its influencing factors among adults in Fujian, China.Study designA cross-sectional study.MethodsFrom September to December 2021, a total of 2,440 Chinese urban and rural adults from Fujian Province, located in Southeastern China, were randomly selected for this cross-sectional study. The probability proportionate approach to sampling was used. A 38-item questionnaire that covered demographics and basic knowledge of cancer, including concepts, screening, therapy, and rehabilitation-related key points was used to measure knowledge levels of cancer prevention and control measures among 2,074 participants. The level of each participants' core knowledge of cancer prevention and control measures was defined as a rate calculated by the number of correct answers divided by the total number of questions. The binary logistic regression model was used to determine if influencing factors were associated with core knowledge awareness.ResultsIn total, 1,290 participants (62.2%) were in the low knowledge group and 784 (37.8%) were in the high knowledge group. The average knowledge rate of cancer prevention and control measures among all participants was 56.01%. Participants from urban areas, who held white-collar jobs, were married, had a bachelor's degree or above, had a family history of cancer, or self-rated their health level as good or average were associated with higher rates of cancer prevention and control core knowledge (overall p < 0.05).ConclusionThese findings may assist healthcare providers and/or researchers in designing effective primary preventive interventions to enhance the general population's cancer prevention and control knowledge, and subsequently decrease the cancer burden in China.

Although treatment outcome in prize-based contingency management has been shown to depend on reinforcement schedule, the optimal schedule is still unknown. Therefore, we conducted a retrospective analysis of data from a randomized clinical trial (Ghitza et al., 2007) to determine the effects of the probability of winning a prize (low vs. high) and the size of the prize won (small, large, or jumbo) on likelihood of abstinence until the next urine-collection day for heroin and cocaine users (N = 116) in methadone maintenance. Higher probability of winning, but not the size of individual prizes, was associated with a greater percentage of cocaine-negative, but not opiate-negative, urines. DESCRIPTORS: computer automation, contingency management, methadone, operant conditioning, opiate, substance abuse

An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a user-specified skeleton, called an event structure, which consists of a number of variables representing events and temporal constraints among these variables; the goal of the discovery is to find temporal patterns, i.e., instantiations of the variables in the structure that appear frequently in the time sequence. This paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern-discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set. Index Terms - Data mining, knowledge discovery, time sequences, temporal databases, time granularity, temporal constraints, temporal patterns.

Increased low-density lipoprotein cholesterol (LDL-C) is the most crucial risk factor for atherosclerotic cardiovascular disease (ASCVD). Statins are the mainstay therapy, but many patients need to add non-statin treatment to reach the recommended LDL-C goal. Although proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors are the most effective agents in LDL-C reduction, they are much more expensive than other lipid-lowering agents. In January 2020, the Taiwan National Health Insurance (NHI) program started to reimburse PCSK9 inhibitors for select ASCVD patients with certain conditions. Major guidelines or consensus worldwide also provide specific recommendations about how to appropriately use these agents. This review summarizes the Taiwan NHI regulations of using PCSK9 inhibitors and compared them with other guidelines or consensus around the world.

Pharmacies have become essential components in support of clinical research. Their operations become highly complex when preponderance of prescriptions is composed of controlled substances. Application of informatics will result in more efficient operations. We present the Pharmacy Information Management System (PIMS) that includes a set of decision support systems to address the pharmacy challenges and is integrated into our electronic health record system.

A clinical recruiting management system with qualification decision support systems was developed to increase the efficiency of screening and evaluation of participants during a recruiting process whereby recruiting for various protocols are conducted at multiple sites by different groups with process interdependencies. This system is seamlessly integrated into our enterprise-scale Human Research Information System (HuRIS), encompassing research participants' electronic health records (EHR), with real-time access to the clinical trial data.

In psychological research, efforts to capture day-to-day human experience traditionally relied on pen-and-paper diaries and questionnaires. Some current studies, however, incorporate handheld computers, which provide researchers with many options and advantages in addition to providing more reliable data. One advantage of using handheld computers is the programmability of the electronic diary, which, compared to old-fashioned paper diaries, affords the researchers with a wealth of possibilities. An important possibility is to construct a built-in mechanism in the computer-administered questionnaires that would allow transparent branching, in which question presentation is contingent on participants' answers to previous questions. The major hurdle in implementing such an approach is the limitations of the platform used for such assessments: inexpensive "low-end" handheld devices. We propose a high-level specification which enables non-programming researchers to "branch" their questionnaires without modifications to the source code in a highly user-friendly fashion, with backtracking capability and very modest hardware requirements. A finite state automaton approach was implemented, we believe for the first time, to create an auto-trigger mechanism for the real-time evaluation of the conditions. This solution provides our investigators with the capacity to administer efficient assessments that are dynamically customized to reflect participants' behaviors without the need for any post-production programming.