Total Number Of Opportunities Research Articles

Multimodal spatial availability: A singly-constrained measure of accessibility considering multiple modes.

Place-based accessibility measures communicate the potential interaction with opportunities at a zone that populations can access. Recent research has explored the implications of how opportunities are counted by different accessibility methods. In conventional measures, opportunities are multiply counted if more than one zone offers access to the same opportunity. This multi-count of opportunities leads to values of accessibility that are difficult to interpret. A possible solution to enhance the meaning-making of accessibility results is by constraining the calculations to match a known quantity. This ensures all zonal values sum up to a predetermined quantity (i.e., the total number of opportunities). In this way, each value can be meaningfully related to this total. A recent effort that implements this solution is spatial availability, a singly-constrained accessibility measure. In this paper, we extend spatial availability for use in the case of multiple modes or more generally, heterogeneous population segments with distinct travel behaviors. After deriving a multimodal version of spatial availability, we proceed to illustrate its features using a synthetic example. We then apply it to an empirical example of low emission zones in Madrid, Spain. We conclude with suggestions for future research and its use in evaluating policy interventions.

Appropriate Number of Observations to Determine Hand Hygiene Compliance Among Healthcare Workers

Background: We sought to determine the minimum number of observations needed to determine hand hygiene (HH) compliance among healthcare workers. Methods: The study was conducted at a referral hospital. We retrospectively analyzed the result of HH monitoring from January to December 2018. HH compliance was calculated by dividing the number of observed HH actions by the total number of opportunities. Appropriate HH compliance rates were calculated based on the 6-step technique, modified from the World Health Organization (WHO) recommendation. The minimum number of required observations (n) was calculated by the following equation using overall mean value (r), absolute precision (d), and confidence interval (1-α) [The equation: n3 Zα/22×ρ×1-ρ/d2]. We considered ds of 5%, 10%, 20%, and 30%, with CIs of 99%, 95%, and 90%, respectively. Among the various cases, we focused on 10% for d and 95% for CI. Results: During the study period, 8,791 opportunities among 1,168 healthcare workers were monitored. The mean HH compliance and appropriate HH compliance rates were 80.3% and 59.7%, respectively (Table 1). The minimum number of observations required to determine HH compliance rates ranged from 2 (d, 30%; CI, 90%) to 624 (d, 5%; CI, 99%), and the minimum number of observations for optimal HH compliance ranged from 5 (d, 30%, CI, 90%) to 642 (d, 5%; CI, 99%) (Figure 1). At 10% absolute precision with 95% confidence, the minimum number of observations to determine HH and optimal HH compliance were 61 and 92, respectively. Conclusions: The minimum number of observations to determine HH compliance varies widely according to setting, but at least 5 were needed to determine optimal HH compliance.Funding: NoDisclosures: None

Hand hygiene in surgery in Benin: opportunities and challenges

BackgroundHand Hygiene (HH) has been described as the cornerstone and starting point in all infection control. Compliance to HH is a fundamental quality indicator. The aim of this study was to investigate the HH compliance among Health-care Workers (HCWs) in Benin surgical care units.MethodsA multicenter prospective observational study was conducted for two months. The World Health Organization (WHO) Hand Hygiene Observation Tool was used in obstetric and gastrointestinal surgery through six public hospitals in Benin. HH compliance was calculated by dividing the number of times HH was performed by the total number of opportunities. HH technique and duration were also observed.ResultsA total of 1315 HH opportunities were identified during observation period. Overall, the compliance rate was 33.3% (438/1315), without significant difference between professional categories (nurses =34.2%; auxiliaries =32.7%; and physicians =32.4%; p = 0.705). However, compliance rates differed (p < 0.001) between obstetric (49.4%) and gastrointestinal surgery (24.3%). Generally, HCWs were more compliant after body fluid exposure (54.5%) and after touching patient (37.5%), but less before patient contact (25.9%) and after touching patient surroundings (29.1%).HCWs were more likely to use soap and water (72.1%) compared to the alcohol based hand rub solution (27.9%). For all of the WHO five moments, hand washing was the most preferred action. For instance, hand rub only was observed 3.9% after body fluid exposure and 16.3% before aseptic action compared to hand washing at 50.6 and 16.7% respectively. Duration of HH performance was not correctly adhered to 94% of alcohol hand rub cases (mean duration 9 ± 6 s instead of 20 to 30 s) and 99.5% of hand washing cases (10 ± 7 s instead of the recommended 40 to 60 s). Of the 432 HCWs observed, 77.3% followed HH prerequisites (i.e. no artificial fingernails, no jewellery). We also noted a lack of permanent hand hygiene infrastructures such as sink, soap, towels and clean water.ConclusionCompliance in surgery was found to be low in Benin hospitals. They missed two opportunities out of three to apply HH and when HH was applied, technique and duration were not appropriate. HH practices should be a priority to improve patient safety in Benin.

Research advances in intervening opportunity class models for predicting human mobility

Predicting human mobility between locations is of great significance for investigating the population migration, traffic forecasting, epidemic spreading, commodity trade, social interaction and other relevant areas. The intervening opportunity (IO) model is the model established earliest from the perspective of individual choice behavior to predict human mobility. The IO model takes the total number of opportunities between the origin location and the destination as a key factor in determining human mobility, which has inspired researchers to propose many new IO class models. In this paper, we first review the research advances in the IO class models, including the IO model, radiation class models, population-weighted opportunity class models, exploratory IO class models and universal opportunity model. Among them, although the IO model has an important theoretical value, it contains parameters and has low prediction accuracy, so it is rarely used in practice. The radiation class models are built on the basis of the IO model on the assumption that the individual will choose the closest destination whose benefit is higher than the best one available in origin location. The radiation class models can better predict the commuting behavior between locations. The population-weighted opportunity class models are established on the assumption that when seeking a destination, the individual will not only consider the nearest locations with relatively large benefits, but also consider all locations in the range of alternative space. The population-weighted opportunity class models can better predict intracity trips and intercity travels. The exploratory IO class models are built on condition that the destination selected by the individual presents a higher benefit than the benefit of the origin and the benefits of the intervening opportunities. The exploratory IO class models can better predict the social interaction between individuals, intracity trips and intercity travels. The universal opportunity model is developed on the assumption that when an individual selects a destination, she/he will comprehensively compare the benefits between the origin and the destination and their intervening opportunity. The universal opportunity model presents a new universal framework for IO class models and can accurately predict the movements on different spatiotemporal scales. The IO class models have also been widely used in many fields, including predicting trip distribution in transportation science, modeling the purchasing behaviors of consumers in economics, detecting complex network communities in network science, measuring spatial interaction in economic geography and predicting infectious disease transmission in epidemiology. This paper focuses on the applications of IO class models in spatial interaction and epidemic spreading, and finally presents the discussion on the possible future research directions of these models.

Impact of an intervention on the hand hygiene compliance rates in paediatric surgical intensive care units in two tertiary care hospitals

Hand hygiene (HH) is comprehensively recognized to be the single most significant measure to prevent cross-transmission of microorganisms between patients. This study aimed to measure the HH compliance rates baseline, identify the barriers for HH non-compliance and to assess the impact of multidisciplinary intervention. An interventional study conducted from March 2017 to July 2017 in two surgical paediatric intensive care units (PICU) in two tertiary care hospitals. Root Cause Analysis (RCA) was used for identification of the barriers that affect HH compliance followed by intervention included HH training and education, video presentations in the hospital internal circuit, visual cues, using a code word for gentle verbal reminding and HH champions for follow up. Direct observation of HH was done before and after the intervention applying the WHO HH observation method. HH compliance rate was calculated by dividing the number of HH actions by the total number of opportunities. Chi-square (χ2) test was used to identify statistical significance. A total of 1735 HH opportunities were observed during all phases of the study. RCA identified skin dryness, forgetfulness and work intensity as the commonest barriers for non-compliance. Significant improvement was observed among all groups in PICU A (+23%; p value <0.001); (+19% for nurses, +29 % for doctors, and +36% for others; p value <0.001). While significant improvement was reported among doctors (+28%; p value <0.001) in PICU B. Improvement in HH compliance is achievable by multidisciplinary intervention. Providing continuous intensified training and feedback is essential to maintain high level of HH compliance.

Can medical students calculate drug doses?

AbstractObjectives: A doctor's ability to calculate drug doses is a skill that is generally assumed. We assessed medical students' performance when given four types of dosing calculations typical of those required in an emergency setting.Design: Longitudinal study.Setting and subjects: Students were assessed at the beginning of the third year, and repeatedly during the third and fourth year while receiving training in dosage calculations. Competence was defined as correctly answering all four categories of calculation at any one time, i.e. a score of 100%. Failure to respond correctly to the individual questions was also analysed because an incorrect calculation could be equated with a “patient” receiving a wrong dose.Outcome measures: Outcome measures were the percentage of students achieving competence and the proportion of times students showed competence relative to their total number of opportunities. A further outcome was the percentage of calculations incorrect i.e. potential “patients” harmed.Resu...

Ranking opportunity profiles through dependent evaluation of policies

Rankings to evaluate opportunity distributions present in most of the literature judge a policy (change from one distribution of opportunities to another) on the basis of the changes created and, thus, independently of the original situation. This paper proposes a group of axioms capturing the idea that rankings of equality of opportunities might consider not only the changes promoted, but also the initial situation in society. The combination of this group of axioms with other well-established properties enables us to characterize two families of new opportunity distribution rankings. The first family weighs each individual’s percentage share in the total number of opportunities, while the second weighs opportunities depending on how many agents have them available.