The prevalence rate of those overweight, as well as obesity among children in Saudi Arabia, keeps rising. The aim of the study was to estimate childhood obesity in the Eastern Province, Saudi Arabia. Over the period 2016 to 2017, a cross-sectional campaign was carried out in the Eastern Province of Saudi Arabia. Data were collected from over 20,000 boys’ and girls’ schools. The campaign collected data on birthday/age, weight, height, sex, district in which the school is located, level of education, and blood pressure level. The findings from the present study indicated a prevalence of 25.7% for overweight and obesity among high school-age students. More importantly, ≈35% of the study’s students have either elevated blood pressure or hypertension. The significant predictors of childhood obesity were education level, age, glucose level, and blood pressure. The children in higher school levels originally from the Eastern Province had a high prevalence of overweight and obesity. Recommendations are made on the need of regular screening program among school-age children, as well as to continue raising awareness about childhood obesity.

Objectives: The purpose of this study was to examine official healthcare informatics applications in Saudi Arabia in the context of their role in addressing the coronavirus disease 2019 (COVID-19) pandemic.Methods: This is a case study of official healthcare informatics programs and applications (apps) developed in Saudi Arabia before and during the COVID-19 pandemic. The qualitative content analysis (QCA) method was used. Data collection consisted of two components: a desktop review of documents and actual testing of the programs. According to the QCA method, we developed a matrix for abstracting information on different apps and programs in order to categorize the data. The compilation of information and discussion were based on information summarized in the matrix.Results: Six apps in total were developed before the COVID-19 pandemic. With the advent of the COVID-19 pandemic, three of the apps, SEHA, Mawid, and Sehaty were modified to address different aspects of the pandemic. Both SEHA and Mawid included information about COVID-19 awareness. During the COVID-19 pandemic, three official apps were developed: Tawakkalna, Tetamman, and Tabaud. The Tawakkalna app is mandatory for all citizens and residents to activate when visiting stores and institutions. It has a wide range of COVID-19 and other health-related functions. The Tetamman app provides COVID-19 test results and allows one to check his or her daily symptoms. It also has an educational content library and provides alerts. The Tabaud app notifies individuals if they have been exposed to COVID-19. The features, advantages, and disadvantages of all of the apps were examined.Conclusions: Overall, there were more strengths than shortcomings in the role played by healthcare informatics in the handling of the COVID-19 pandemic in Saudi Arabia.

Background: Medication errors can cause severe injuries and may lead to death. Electronic health records (EHRs) that are well designed and implemented could help to reduce medication errors. The medication management process needs close study to understand how medication safety metrics evolve as hospitals mature in terms of their EHR implementation. Objective: To examine the effect of adopting EHRs on medication errors at the Royal Commission Hospital in Jubail, Saudi Arabia, a Health Information Management System Society (HIMSS) stage 6 hospital. Methods: This study had a quasi-experimental time-series design. Retrospective data were collected for 1.5-year periods before and after implementation of EHRs. The variables analyzed were obtained from various units in the study setting. Data on medication errors were collected from the risk management section of the quality department. The medication management process was studied qualitatively. The quantitative data were analyzed using descriptive and inferential statistics. Results: The median number of medication orders per patient showed a significant decrease, from 22.76 before EHR implementation to 18.76 after implementation (p < 0.001). The median number of incidents per patient showed a significant increase, from 0.029 before to 0.040 after implementation (p = 0.004). The qualitative analysis of processes involved in the medication management process helped to explain these changes. Conclusion: Contrary to expectations, this study showed that an HIMSS stage 6 hospital could experience an increase in medication errors following implementation of EHRs. Qualitative analysis showed that the increase in medication error reporting rate could be attributed to an increase in detection following improvement in the medication management process. This has implications for interpreting quality metrics as hospitals mature in terms of their EHR implementation. RÉSUMÉ Contexte : Les erreurs de médication peuvent causer des blessures graves et entraîner la mort. La bonne conception et la mise en place de dossiers de santé électroniques (DSE) pourraient aider à les réduire. Le processus de gestion des médicaments doit faire l’objet d’un examen attentif pour comprendre comment les paramètres de sécurité relatifs aux médicaments évoluent à mesure que les hôpitaux se modernisent grâce à la mise en place de DSE. Objectifs : Examiner l’effet de l’adoption des DSE sur les erreurs de médication au Royal Commission Hospital de Jubail, en Arabie saoudite, un hôpital de stade 6 de la Health Information Management System Society (HIMSS). Méthodes : Cette étude utilisait une méthodologie de série chronologique quasi expérimentale. Des données rétrospectives ont été recueillies pendant des périodes de 1,5 an avant et 1,5 an après la mise en place des DSE. Les variables analysées ont été obtenues à partir de diverses unités dans le cadre de l’étude. Les données sur les erreurs de médication ont été recueillies auprès de la section de gestion des risques du service qualité. Le processus de gestion des médicaments, quant à lui, a été étudié de manière qualitative. Les données quantitatives ont été analysées à l’aide de statistiques descriptives et inférentielles. Résultats : Le nombre médian d’ordonnances médicales par patient a fortement diminué, passant de 22,76 avant à 18,76 après la mise en place des DSE (p < 0,001). Le nombre médian d’incidents par patient a quant à lui augmenté de manière importante et est passé de 0,029 avant à 0,040 après la mise en place des DES (p = 0,004). Les résultats de l’analyse qualitative des étapes du processus de gestion des médicaments expliquent en partie ces changements. Conclusion : Contrairement aux attentes, cette étude a montré qu’un hôpital de stade 6 de la HIMSS pourrait connaître une augmentation des erreurs de médication à la suite de la mise en place de DSE. L’analyse qualitative a montré que l’augmentation du taux de déclaration des erreurs de médication pouvait être attribuée à une augmentation de la détection suivant l’amélioration du processus de gestion des médicaments. Ce constat a des implications pour l’interprétation des indicateurs de la qualité à mesure que les hôpitaux se modernisent en mettant en place des DSE.

The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the “Sharik” Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using the predictors from the logistic regression model. Accuracy measures were checked using training, validation, and testing data. Females and smokers had the highest likelihood of experiencing multimorbidity. Age and fruit consumption also played a significant role in predicting multimorbidity. Regarding model accuracy, both logistic regression and backpropagation algorithms yielded comparable outcomes. The backpropagation method (accuracy 80.7%) was more accurate than the logistic regression model (77%). Machine learning algorithms can be used to predict multimorbidity among adults, particularly in the Middle East region. Different testing methods later validated the common predicting factors identified in this study. These factors are helpful and can be translated by policymakers to consider improvements in the public health domain.

Over the period 2016-2030, Saudi Arabia seeks to increase life expectancy at birth by 6 years. The main aim was to analyse the feasibility of this gain using the demographic literature. The demographic literature on recorded high gains in life expectancy and the determinants of these gains were reviewed. The findings were examined considering the Saudi Arabian context. Explanations covered demographic and behavioural factors, inequalities, health care delivery system, public health provision, health-related policies and advancements in science and technology. However, the crucial factor is the country's position in the "cardiovascular revolution" (which combines smoking prevalence, obesity, lifestyle and related policies). In countries where life expectancy is in the 70s, average yearly gains are less than 0.31 years, much lower that the Saudi Arabian target of 0.43 years. For Saudi Arabia, an average yearly gain of about half this is achievable provided the fundamental drivers of life expectancy are addressed.