This study aimed to analyze and relate the population interest through information search trends on Nutrition and Healthy Diet (HD) with the Occupational Health (OH). Ecological and correlational study of the Relative Search Volume (RSV) obtained from Google Trends query, segmented in two searched periods concerning antiquity; date of query: 20 April 2021. The RSV trends for the analyzed three Topics were: Nutrition (R2 = 0.02), HD (R2 = 0.07) and OH (R2 = −0.72). There was a good positive correlation between Nutrition and OH (R = 0.56, p < 0.001) and a moderate one between HD and OH (R = 0.32, p < 0.001). According to seasons, differences were verified between RSV means in the Topics HD (p < 0.01) and OH (p < 0.001). Temporal dependence was demonstrated on Nutrition searches (Augmented Dickey–Fuller = −2.35, p > 0.05). There was only a significant relationship between the RSV Topic HD (p < 0.05) for the Developing and Least Developed countries. The data on the analyzed RSV demonstrated diminishing interest in the search information on HD and OH as well as a clearly positive trend change in recent years for Nutrition. A good positive correlation was observed between the RSV of nutrition and OH whereas the correlation between HD and OH was moderate. There were no milestones found that may report a punctual event leading to the improvement of information searches. Temporal dependence was corroborated in the RSV on Nutrition, but not in the other two Topics. Strangely, only an association was found on HD searches between the Developing and Least Developed Countries. The study of information search trends may provide useful information on the population’s interest in the disease data, as well as would gradually allow the analysis of differences in popularity, or interest even between different countries. Thus, this information might be used as a guide for public health approaches regarding nutrition and a healthy diet at work.
Objective To assess the perception of risk of exposure in the management of hazardous drugs (HDs) through home hospitalization and hospital units. Material and methods A questionnaire was released, at the national level, to health professionals with HD management expertise. Questionnaire included 21 questions that were scored using a Likert scale: 0 (null probability) to 4 (very high probability). The internal consistency and reliability of the questionnaire were calculated using Cronbach’s alpha and the intraclass correlation coefficient, respectively. Results 144 questionnaires (response rate 70.2%) were obtained: 65 (45.1%) were nurses, 42 (28.9%) occupational physicians, and 37 (26.1%) were pharmacists. Cronbach’s alpha was 0.93, and intraclass correlation coefficient was 0.94 (95% CI 0.91–0.97; p-value < 0.001). The mean probability was 1.95 ± 1.02 (median 1.9; minimum: 0.05; 1st quartile 1.1; 3rd quartile 2.6; and maximum 4). Differences were observed in scoring among professional groups (occupational physicians versus nurses (1.6/2.1, p = 0.044); pharmacists versus nurses (1.7/2.1, p = 0.05); and occupational physicians versus pharmacists (1.6/1.7, p = 0.785), due mainly to the administration stage (p = 0.015). Conclusions The perception of risk of exposure was moderate, being higher for nurses. It would be advisable to integrate HDs into a standardized management system (risk management model applicable to any healthcare center) to improve the safety of health professionals.
Background: Data analysis techniques such as machine learning have been used for assisting in triage and the diagnosis of health problems. Nevertheless, it has not been used yet to assist community pharmacists with services such as the Minor Ailment Services These services have been implemented to reduce the burden of primary care consultations in general medical practitioners (GPs) and to allow a better utilization of community pharmacists’ skills. However, there is a need to refer high-risk patients to GPs.Aim: To develop a predictive model for high-risk patients that need referral assisting community pharmacists’ triage through a minor ailment service.Method: An ongoing pragmatic type 3 effectiveness-implementation hybrid study was undertaken at a national level in Spanish community pharmacies since October 2020. Pharmacists recruited patients presenting with minor ailments and followed them 10 days after the consultation. The main outcome measured was appropriate medical referral (in accordance with previously co-designed protocols). Nine machine learning models were tested (three statistical, three black box and three tree models) to assist pharmacists in the detection of high-risk individuals in need of referral.Results: Over 14′000 patients were included in the study. Most patients were female (68.1%). With no previous treatment for the specific minor ailment (68.0%) presented. A percentage of patients had referral criteria (13.8%) however, not all of these patients were referred by the pharmacist to the GP (8.5%). The pharmacists were using their clinical expertise not to refer these patients. The primary prediction model was the radial support vector machine (RSVM) with an accuracy of 0.934 (CI95 = [0.926,0.942]), Cohen’s kappa of 0.630, recall equal to 0.975 and an area under the curve of 0.897. Twenty variables (out of 61 evaluated) were included in the model. radial support vector machine could predict 95.2% of the true negatives and 74.8% of the true positives. When evaluating the performance for the 25 patient’s profiles most frequent in the study, the model was considered appropriate for 56% of them.Conclusion: A RSVM model was obtained to assist in the differentiation of patients that can be managed in community pharmacy from those who are at risk and should be evaluated by GPs. This tool potentially increases patients’ safety by increasing pharmacists’ ability to differentiate minor ailments from other medical conditions.
(1) Objective: to identify and review existing infodemiological studies on nutritional disorders applied to occupational health and to analyse the effect of the intervention on body mass index (BMI) or alternatively body weight (BW); (2) Methods: This study involved a critical analysis of articles retrieved from MEDLINE (via PubMed), Embase, Cochrane Library, PsycINFO, Scopus, Web of Science, Latin American, and Caribbean Health Sciences Literature (LILACS) and Medicina en Español (MEDES) using the descriptors “Nutrition Disorders, “Occupational Health” and “Infodemiology”, applying the filters “Humans” and “Adult: 19+ years”. The search was conducted on 29 May 2021; (3) Results: a total of 357 references were identified from the bibliographic database searches; after applying the inclusion and exclusion criteria, a total of 11 valid studies were obtained for the review. Interventions could be categorised into (1) interventions related to lifestyle, physical activity, and dietary changes through education programmes, (2) interventions associated with lifestyle, physical activity, and dietary changes through the use of telemonitoring systems or self-help applications, (3) interventions tied to lifestyle, physical activity, and dietary changes through control and/or social network support groups, and (4) interventions linked to changes in the work environment, including behavioural change training and work environment training tasks. The meta-analysis demonstrated that the heterogeneity present when analysing the results for BMI was 72% (p < 0.01), which decreased to 0% (p = 0.57) when analysing the outcomes for weight, in which case the null hypothesis of homogeneity could be accepted. In all instances, the final summary of the effect was on the decreasing side for both BMI and BW; (4) Conclusions: Despite the high heterogeneity of the results reported, the trend shown in all cases indicates that the intervention methodologies implemented by empowering individuals through Web 2.0 technologies are positive in terms of the problem of overweight. Further implementation of novel strategies to support individuals is needed to overcome obesity, and, at least in the early studies, these strategies seem to be making the necessary change.
Objetivo: Analizar la utilización de los Descriptores, como Major Topic, en la indización de los artículos sobre Hospitalización Domiciliaria en la base de datos MEDLINE.Método: Estudio descriptivo transversal de los registros de indización recogidos en la base de datos MEDLINE (vía PubMed) hasta 2016. El término utilizado, como descriptor principal para la búsqueda fue «Home Care Services, Hospital-Based».El método de muestreo fue la aleatorización simple sin reemplazo, tomando como base el número total de referencias obtenidas (tamaño muestral 386).Resultados: Se observaron diferencias significativas en la utilización de los Descriptores asociados a hospitalización a domicilio. La comparativa entre descriptores dio una p<0,004 y una razón de verosimilitudes de 727,52 con 460 grados de libertad.Conclusiones: Los descriptores utilizados son adecuados a la temática estudiada, Hospital a Domicilio. Lógicamente, «Home Care Services, Hospital-Based» es el Descriptor con mayor frecuencia de uso, ya que es el utilizado para realizar la búsqueda. El aumento en el diagnóstico de las neoplasias genera la aparición de «Neoplasms» como el segundo descriptor más usado y el auge de la informática y la tecnología hace que «Telemedicine» aparezca como tercer descriptor.
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