Effectiveness of Prevention Interventions Using Social Marketing Methods on Behavioural Change in the General Population: A Systematic Review of the Literature.

In an effort to encourage people to adopt healthy behaviours, social marketing is increasingly used in disease prevention and health promotion. This systematic review aimed to evaluate the effect of prevention initiatives that use social marketing techniques on achieving behavioural change in the general population. We conducted a systematic review of PubMed, Embase, Science Direct, Cochrane, and Business Source Complete. Among 1189 articles identified across all databases, 10 studies met the inclusion criteria (six randomized controlled trials and four systematic reviews). The number of social marketing criteria used varies according to the studies. The results showed positive effects overall, albeit not always statistically significant. The quality of the studies was mixed: 3/4 of the systematic reviews did not meet the methodological criteria, and four out of six randomized trials had at least a high risk of bias. Social marketing is not fully exploited in prevention interventions. However, the greater the number of social marketing criteria used, the more positive the effects observed. Social marketing thus appears to be an interesting concept to bring about behavioural change, but it requires rigorous monitoring to ensure maximum effectiveness.

Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods.

BACKGROUND: Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between their hospitals. The objective of this study was to identify distinctive profiles of French hospitals according to their characteristics and their role in the French hospital network. METHODS: Data were extracted from the national hospital database for year 2016. The database was restricted to public hospitals that practiced medicine, surgery or obstetrics. Hospitals profiles were determined using the k-means method. The variables entered in the clustering algorithm were: the number of stays, the effective diversity of hospital activity, and a network-based mobility indicator (proportion of stays followed by another stay in a different hospital of the same Regional Hospital Group within 90 days). RESULTS: Three hospital groups were identified by the clustering algorithm. The first group was constituted of 34 large hospitals (median 82,100 annual stays, interquartile range 69,004 - 117,774) with a very diverse activity. The second group contained medium-sized hospitals (with a median of 258 beds, interquartile range 164 - 377). The third group featured less diversity regarding the type of stay (with a mean of 8 effective activity domains, standard deviation 2.73), a smaller size and a higher proportion of patients that subsequently visited other hospitals (11%). The most frequent type of patient mobility occurred from the hospitals in group 2 to the hospitals in group 1 (29%). The reverse direction was less frequent (19%). CONCLUSIONS: The French hospital network is organized around three categories of public hospitals, with an unbalanced and disassortative patient flow. This type of organization has implications for hospital planning and infectious diseases control.

Forecasting the Emergency Department Patients Flow.

Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.