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Many patients referred by their GP for an assessment by secondary mental health services are unlikely to ever meet eligibility thresholds for specialist treatment and support. A new service was developed to support people in primary care. “the authors evaluate” whether the phased introduction of the Lambeth Living Well Network (LWN) Hub to a population in south London led to: a reduction in the overall volume of patients referred from primary care for a secondary mental health care assessment; and an increase in the proportion of patients referred who met specialist service eligibility criteria, as indicated by the likelihood of being accepted in secondary care.

The evaluation applied a quasi-experimental interrupted time series design using electronic patient records data for a National Health Service (NHS) provider of secondary mental health services in south London.

Scale-up of the Hub to the whole of the population of Lambeth led to an average of 98 fewer secondary care assessments per month (95% CI −118 to −78) compared to an average of 203 assessments per month estimated in the absence of the Hub; and an absolute incremental increase in the probability of acceptance for specialist intervention of 0.20 (95% CI; 0.14 to 0.27) above an average probability of acceptance of 0.57 in the absence of the Hub.

Mental health outcomes for people using the service and system wide-service impacts were not evaluated preventing a more holistic evaluation of the effectiveness and cost-effectiveness of the LWN Hub.

Providing general practitioners with access to service infrastructure designed to help people whose needs cannot be managed within specialist mental health services can prevent unnecessary referrals into secondary care assessment teams.

Reducing unnecessary referrals through provision of a primary-care linked mental health service will reduce delay in access to professional support that can address specific mental-health related needs that could not be offered within the secondary care services and could prevent the escalation of problems.

The authors use NHS data to facilitate the novel application of a quasi-experimental methodology to deliver new evidence on whether an innovative primary care linked mental health service was effective in delivering on one of its key aims.

This study investigates the effect of user fees on access and waiting time in Nigeria. For access, the effect of user fees on both preventive and curative care; and the effect of user fees on waiting time at public healthcare facilities were examined. User fees are vital for the fiscal sustainability of healthcare provision for most African economies. Its imposition could debar healthcare access by the poor while its removal can reduce quality of care and induce longer waiting time.

The wave 3 of the Nigerian General Household Survey (2015/16) data was used for users of public health facilities. Access to healthcare was modelled using utilization data in a logistic regression model while waiting time was through the Negative Binomial Regression Model (NBRM).

The analyses showed significant effects of user fees on access to both preventive and curative care and on time spent waiting to make use of healthcare services. Individuals were able to access healthcare services regardless of amounts paid. Also, there was a non-negative effect of user fee imposition on waiting time.

Nigeria should improve healthcare facilities to address the enormous demand for healthcare services when designing policy for health sector.

This paper shows that even with the imposition of user fees, healthcare facilities could still not cater for the rising healthcare needs of the populace but cautioned that its abolition may not be a preferred option.

The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.

The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE).

The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models.

The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

Different countries have responded to the pandemic with distinct domestic and international travel restrictions. The purpose of this paper is to investigate the stringency of the coronavirus disease 2019 (COVID-19) countermeasures in Japan against their G20 cohorts. Primary data were monitored at a ski resort in Kyushu regarding the social acceptance of initial COVID-19 countermeasures, ranging from hygiene and local “lockdowns” to border control measures.

The stringency of the COVID-19 countermeasures was examined using data from the Oxford COVID-19 Government Response Tracker (OxCGRT) and triangulated with the early stage social acceptance of survey respondents in Aso Kuju National Park in February 2020 that consisted of 165 valid Japanese language questionnaires.

An one-way analysis of variance (ANOVA) identified significant differences in social acceptance for countermeasures, with more-concerned respondents agreeing more strongly with “low-tech” health protocols, such as washing hands (M = 3.7) or wearing a mask (3.4). More concerned visitors were significantly more likely to modify their travel plans (2.9) or cancel their trip altogether (2.7). Male day trippers were less likely to be concerned by the COVID-19 pandemic.

This paper's originality is derived from a triangulation of the stringency of Japan's initial COVID-19 countermeasures via a combination of comparison with G20 cohorts and social acceptance of domestic snowboarders and skiers. Moreover, by shining a light on the trade-off between public health and human rights, the paper provides a current review of the ethical dimension of a travel restriction debate that is often overlooked in the ongoing pandemic.

This study aims to perform a benefit segmentation and then a classification of visitors that travel to the Rocha Department in Uruguay from the capital city of Montevideo during the summer months.

A convenience sample was obtained with an online survey. A total of 290 cases were usable for subsequent data analysis. The following statistical techniques were used: hierarchical cluster analysis, K-means cluster analysis, machine learning, support vector machines, random forest and logistic regression.

Visitors that travel to the Rocha Department from Montevideo can be classified into four distinct clusters. Clusters are labelled as “entertainment seekers”, “Rocha followers”, “relax and activities seekers” and “active tourists”. The support vector machine model achieved the best classification results.

Implications for destination marketers who cater to young visitors are discussed. Destination marketers should determine an optimal level of resource allocation and destination management activities that compare both present costs and discounted potential future income of the different target markets. Surveying non-residents was not possible. Future work should sample tourists from abroad.

The combination of market segmentation of Rocha Department’s visitors from the city of Montevideo and classification of sampled individuals training various machine learning classifiers would allow Rocha’s destination marketers determine the belonging of an unsampled individual into one of the already obtained four clusters, enhancing marketing promotion for targeted offers.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.