Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries

Influence of Religiosity on Youths' Attitudes Towards People with Disabilities in the United Arab Emirates

This cross-sectional survey investigates the influence of youths' religiosity on their attitude towards people with disabilities. The Muslim religiosity questionnaire and multidimensional attitudes scale towards persons with disabilities were used to survey 733 youths from the federal university in the United Arab Emirates. The results indicated that the youths were religious and had positive attitudes towards people with disabilities. An increase in religiosity is associated with a positive attitude towards disability, and both religiosity and total family income positively impacted the attitude towards people with disabilities. Reducing inequalities by including persons with disabilities is one of the UN 2030 Agenda for Sustainable Development objectives. Policies should aim to enhance curriculum, improvise public guidelines and partner with associated faith-based leaders to build an inclusive society for people with disabilities, thus helping to achieve sustainable development goals.

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention's generalizability and interoperability with existing systems, as well as the inner settings' data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

Has the COVID-19 pandemic converged across countries?

The outbreak of COVID-19 has induced economic and financial disruptions to global economies, consistent with those experienced during previous episodes of economic or financial crises. This study offers a critical perspective into the spread of the virus by investigating the convergence patterns of COVID-19 across 155 countries from March 2020 to August 2021. The club clustering algorithm is used to verify the convergence patterns of infection and death rates in these countries. The findings show that full panel convergence cannot be achieved indicating the presence of sub-convergent clusters. Cluster formation for death rates includes the Americas, Africa, the Middle East, and Asia, among others. To understand the factors driving these results, we analyse the determinants of the convergence process of COVID-19. The probability of belonging to a cluster with higher death intensity increases with being above the age of 65, poverty, and for female smokers while handwashing shows beneficial effect on case intensity.

Analysing 11 years of incidence trends, clinicopathological characteristics, and forecasts of colorectal cancer in young and old patients: a retrospective cross-sectional study in an Indonesian national referral hospital

OBJECTIVE

To obtain annual incidence trends, understand clinicopathological characteristics, and forecast the future burden of colorectal cancer (CRC) in Indonesia.

DESIGN

11-year retrospective cross-sectional study.

SETTING

A national referral hospital in Jakarta, Indonesia.

PARTICIPANTS

Data from 1584 eligible cases were recorded for trends and forecasting analyses; 433 samples were analysed to determine clinicopathological differences between young (<50 years) and old (≥50 years) patients.

METHODS

Trend analyses were done using Joinpoint software, expressed in annual percentage change (APC), and a regression analysis was executed to generate a forecasting model. Patients' characteristics were compared using χ2 or non-parametric tests.

MAIN OUTCOMES

Analysis of trends, forecasting model, and clinicopathological features between the age groups.

RESULTS

A significant increase in APC was observed among old patients (+2.38%) for CRC cases. Colon cancer increased remarkably (+9.24%) among young patients; rectal cancer trends were either stable or declining. The trend for right-sided CRC increased in the general population (+6.52%) and old patients (+6.57%), while the trend for left-sided CRC was stable. These cases are expected to be a significant health burden within the next 10 years. Patients had a mean age of 53.17±13.94, 38.1% were young, and the sex ratio was 1.21. Prominent characteristics were left-sided CRC, tumour size ≥5 cm, exophytic growth, adenocarcinoma, histologically low grade, pT3, pN0, inadequately dissected lymph nodes (LNs), LN ratio <0.05, no distant metastasis, early-stage cancer, no lymphovascular invasion, and no perineural invasion (PNI). Distinct features between young and old patients were found in the histological subtype, number of dissected LN, and PNI of the tumour.

CONCLUSIONS

Epidemiological trends and forecasting analyses of CRC cases in Indonesian patients showed an enormous increase in colon cancer in young patients, a particularly concerning trend. Additionally, young patients exhibited particular clinicopathological characteristics that contributed to disease severity.

Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections— Performance evaluation

COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.

Medication Adherence Among Patients with Multimorbidity in the United Arab Emirates

Background

Multimorbidity, defined as having two or more chronic diseases, has a major impact on public health and Sustainable Development Goals (SDG). This study aims to assess the prevalence of medication adherence and associated factors among patients with multimorbidity.

Methods

A questionnaire-based, cross-sectional survey was conducted by a trained interviewer across patients with multimorbidity attending outpatient clinics in two tertiary referral hospitals in the United Arab Emirates (UAE). Demographic and social variables and the outcome (self-reported adherence to long-term medication) were measured using the General Medication Adherence Scale (GMAS). Multiple logistic regression was used to assess medication adherence and associated factors.

Results

From a total of 630 participants included in this study, the estimated prevalence of high medication adherence is 78.57% (±1.63478) with a 95% confidence interval (CI) [75.19, 81.61]. The odds of high medication adherence increased with age. The odds of high medication adherence for patients aged 66 years and older than those aged 19-35 years is adjusted odds ratio (AOR) = 3.880, with a 95% CI [1.124, 13.390]. Patients with income more than 50,000 had the odds, AOR = 5.169 with a 95% CI [1.282, 20.843], compared to those earning less than 10,000 Dirhams (AED). Patients aged 36-65 with health insurance coverage had higher medication adherence than groups on the other end. The number of current medications is significantly (p-value = 0.027) associated with high medication adherence with the odds of high medication adherence, AOR = 4.529 with a 95% CI [1.184, 17.326], the highest for those currently taking four medications.

Conclusion

This study highlights younger population having multimorbidity in the context of an increasing life expectancy and suboptimal therapeutic outcomes. Furthermore, the study highlights multimorbidity is associated with low medication adherence and out-of-pocket payment, and non-availability of insurance is a major hindrance to medication adherence.

Health literacy and fear among Iranian medical students due to COVID-19: An observational study

INTRODUCTION

The coronavirus disease of the 2019 (COVID-19) pandemic has created a sense of fear due to uncertainties in medical students' personal and professional lives. Medical education is challenging and poses a more significant academic and emotional rigor when compared with other professional programs. With the COVID-19 having limited treatment options, health literacy (HL) is crucial for managing and responding to the pandemic. This research aims to examine the impact of HL on COVID-19-associated fear among Iranian medical students.

METHODS

A cross-sectional study was conducted measuring the HL and fear of COVID-19 using validated scales. Two hundred and seventy-eight survey responses were received and analyzed descriptively by using SPSS software inferential statistics.

RESULTS

Mean age of participants was 22.93 ± 5.427 years. The correlation between students' total health literacy (HELIA) scores and their fear of COVID-19 (FCV-19S) scores was -0.279 (p value = .019). The FCV-19S has a negative correlation with total HELIA in the group of females and males, single and married people, bachelor and MD students, and students living in rural areas and urban areas. The negative correlation of FCV-19S with total HELIA in males and students who reside in rural areas was the only one that was not significant. In associate degree students, the correlation between FCV-19S and total HELIA was positive but not significant. Education grades had a significant impact on FCV-19S. Furthermore, the place of residence also had a significant impact on FCV-19S. However, the gender and marital status did not significantly impact FCV-19S.

CONCLUSIONS

The present study showed that students with higher HL scores had lesser fear of COVID-19. The key stakeholders require several positive strategies to reduce fear and improve health, and such vital policies will assist in improving the students' health and achieving the sustainable developmental goals.

Complementary feeding practices and the associated risk of childhood obesity among ethnic minority groups living in high-income countries: protocol for a systematic review and meta-analysis

INTRODUCTION

Complementary feeding (CF) is defined as the period from when exclusive breast milk and formula are no longer sufficient for meeting the infant's nutritional needs. The CF period occurs from birth to 23 months of age. Though the recommended guidelines for introducing CF is from around 6 months of age, data indicates that some infants are introduced to food earlier than 6 months which can predispose children to obesity and overweight. Obesity in ethnic minority groups (EMG) is higher than their native counterparts and often tracks into adulthood. Hence, our aim was to conduct a systematic review and meta-analysis on the available literature to identify the risk of childhood overweight/obesity associated with CF practices concerning their timing, as well as the frequency and type of CF food introduced. We focused specifically on EMG children living in high-income countries.

METHODS AND ANALYSIS

A methodological literature search surrounding childhood obesity and overweight (COO) risk associated with CF practices will be conducted in May 2021 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines. The following academic databases will be methodologically searched: PubMed, EMBASE, PsycINFO, CINAHL, SCOPUS, Cochrane Library and the WHO Global Index Medicus. Three independent researchers will be involved in independent screening and review the included articles based on the predefined inclusion and exclusion criteria. Where conflicts arise during the screening process, it will be resolved through discourse until a consensus is reached. Information on CF practices and anthropometric measurements will be extracted to ascertain the risk of COO. For this study, WHO body mass index for age and sex percentiles, Centers for Disease Control and Prevention classification and other recognised country-specific classifications will be utilised for the outcome.

ETHICS AND DISSEMINATION

Formal ethical approval is not needed as the results will be drawn from currently available published literature. Outcomes of the review will be shared through peer-reviewed publications.

PROSPERO REGISTRATION NUMBER

CRD42021246029.

Systematic review of the effects of pandemic confinements on body weight and their determinants

Pandemics and subsequent lifestyle restrictions such as ‘lockdowns’ may have unintended consequences, including alterations in body weight. This systematic review assesses the impact of pandemic confinement on body weight and identifies contributory factors. A comprehensive literature search was performed in seven electronic databases and in grey sources from their inception until 1 July 2020 with an update in PubMed and Scopus on 1 February 2021. In total, 2361 unique records were retrieved, of which forty-one studies were identified eligible: one case–control study, fourteen cohort and twenty-six cross-sectional studies (469, 362 total participants). The participants ranged in age from 6 to 86 years. The proportion of female participants ranged from 37 % to 100 %. Pandemic confinements were associated with weight gain in 7·2–72·4 % of participants and weight loss in 11·1–32·0 % of participants. Weight gain ranged from 0·6 (sd 1·3) to 3·0 (sd 2·4) kg, and weight loss ranged from 2·0 (sd 1·4) to 2·9 (sd 1·5) kg. Weight gain occurred predominantly in participants who were already overweight or obese. Associated factors included increased consumption of unhealthy food with changes in physical activity and altered sleep patterns. Weight loss during the pandemic was observed in individuals with previous low weight, and those who ate less and were more physically active before lockdown. Maintaining a stable weight was more difficult in populations with reduced income, particularly in individuals with lower educational attainment. The findings of this systematic review highlight the short-term effects of pandemic confinements.

Hypertension Prevalence, Awareness, and Control Among Parents of School-Aged Children in the United Arab Emirates

BACKGROUND

Increased blood pressure (BP) is a major cardiovascular disease risk factor. The study aimed to determine the prevalence and predictors of hypertension and its awareness and control among parents of school-aged children in the United Arab Emirates (UAE).

METHODS

A total of 605 parents participated in this cross-sectional study. Information on socio-demographics, lifestyle factors, and history of chronic disease were collected through an adapted version of the World Health Organization STEPS questionnaire. Fasting blood glucose samples, BP measurements, body mass index (BMI), and waist and hip circumference were obtained using standard measurement protocols. Prevalence of hypertension was identified in the cohorts by defining hypertension using the 2017 American College of Cardiology (ACC) and the American Heart Association (AHA) guidelines (BP≥130/80 mmHg) and the World Health Organization-International Society of Hypertension Guidelines Orchid (BP≥140/90 mmHg) in association with antihypertensive medication use.

RESULTS

The mean age of participants was 42.9±7.9 years. The prevalence of hypertension was 37.2% (95% CI: 33.5-41.2) and 18.0% (95% CI: 15.2-21.3), using the 2017 and the previous WHO definitions, respectively. Little over half of the sample (51.5%) who were aware of having hypertension reported using antihypertensive medications. Of those reporting the use of antihypertensive medications in the past two days, 13 of 33 patients (39.4%) had their hypertension under control (<140/90 mmHg). The independent correlates of hypertension included age [(adjusted odds ratio (AOR): 1.09 (1.05-1.13], male sex [AOR: 2.48 (1.41-4.34], college or higher education [AOR: 0.22 (0.09-0.56)], family history of hypertension [AOR: 2.03 (1.17-3.53)], obesity [AOR: 3.15 (1.24-7.12)], and moderate or vigorous physical activity [AOR: 0.50 (0.26-0.98)].

CONCLUSION

Hypertension is prevalent among parents of school-going children. Improving lifestyle, health literacy, and introducing innovative models to raise awareness and education about hypertension are essential to achieve sustainable development goals (SDGs).

Application of the ARIMA Model to Predict Under-Reporting of New Cases of Hansen's Disease during the COVID-19 Pandemic in a Municipality of the Amazon Region

This work aimed to apply the ARIMA model to predict the under-reporting of new Hansen’s disease cases during the COVID-19 pandemic in Palmas, Tocantins, Brazil. This is an ecological time series study of Hansen’s disease indicators in the city of Palmas between 2001 and 2020 using the autoregressive integrated moving averages method. Data from the Notifiable Injuries Information System and population estimates from the Brazilian Institute of Geography and Statistics were collected. A total of 7035 new reported cases of Hansen’s disease were analyzed. The ARIMA model (4,0,3) presented the lowest values for the two tested information criteria and was the one that best fit the data, as AIC = 431.30 and BIC = 462.28, using a statistical significance level of 0.05 and showing the differences between the predicted values and those recorded in the notifications, indicating a large number of under-reporting of Hansen’s disease new cases during the period from April to December 2020. The ARIMA model reported that 177% of new cases of Hansen’s disease were not reported in Palmas during the period of the COVID-19 pandemic in 2020. This study shows the need for the municipal control program to undertake immediate actions in terms of actively searching for cases and reducing their hidden prevalence.

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries

This study tests the validity of the club convergence clustering hypothesis in the G20 countries using four measures of the spread of the COVID-19 pandemic: total number of confirmed cases per million people, new cases per million people, total deaths per million people, and new deaths per million people. The empirical analysis is based on the daily data from March 1, 2020, to October 10, 2020. The results indicate three clusters for the per capita income, two clusters for total cases per million people, and new cases per million people. Besides, there are only one and two clusters for total deaths per million people and new deaths per million people. Potential policy implications are also discussed in detail.