Amal Mousa Zaidan

Food security in middle-income countries is a growing phenomenon, becoming more relevant than ever before. This study examines the effects of government expenditure, nutrition, and sustainability on health and food security in middle-income countries, with a focus on child stunting under macroeconomic constraints. It measures the impact on the empirical environment, accounting for relevant macroeconomic constraints that affect child stunting. Using the System Generalized Method of Moments (System–GMM) model to control for endogeneity and persistence in food security, a panel data set of 35 middle-income countries over the period 2000–2023 is employed. The results reveal strong persistence in food security dynamics (β = 0.642, p < 0.01). Government health expenditure significantly improves food security (β = −0.481, p < 0.01), whereas inflation (β = 0.074), public debt (β = 0.028), and exchange rate depreciation (β = 0.516) increase food insecurity. Child stunting was positively associated with food insecurity (β = 0.219, p < 0.01), whereas sustainability was associated with improved food security outcomes (β = −0.273, p < 0.05). The long-run effect of government health expenditure (−1.344) substantially exceeds its short-run impact, highlighting the importance of sustained investment. The findings underscore the need for integrated policies that combine public health investment, macroeconomic stability, and sustainability-oriented development to strengthen food security and reduce chronic malnutrition in middle-income countries.

Background: Renal disease represents a significant and growing public health concern globally and within Saudi Arabia. Despite the increasing burden of noncommunicable diseases, population-based data on the prevalence and determinants of renal disease in Saudi Arabia remain limited. Understanding epidemiology, including prevalence and associated risk factors of renal disease in the context of Saudi Arabia, is essential for designing preventive and early detection strategies. Objective: This study aims to estimate the prevalence of renal disease and to identify sociodemographic, behavioral, and clinically associated factors among adults attending primary health care centers (PHCs) in Riyadh, Saudi Arabia. Methods: A cross-sectional, community-based study was conducted between March 2023 and July 2023 across 48 PHCs within Riyadh, using a consecutive sampling strategy. Adults aged 18 years and older were recruited and completed a validated, interviewer-administered electronic questionnaire assessing sociodemographic characteristics, lifestyle behaviors, and medical history, including comorbid conditions and health care access. Multivariable logistic regression was used to determine factors associated with self-reported renal disease, with results expressed as adjusted odds ratios (AORs) and 95% CIs, ensuring model adequacy and precision. Results: A total of 14,239 participants were surveyed (n=7519, 52.8% female; mean age 41.6, SD 13.2 y). The prevalence of self-reported renal disease (including chronic kidney disease and kidney stones) was 3.5% (n=504). Individuals aged younger than 50 years had higher odds of renal disease (AOR 1.22, 95% CI 1.02‐1.47). Female individuals were more likely to report renal disease than males (AOR 1.51, 95% CI 1.24‐1.84). Participants with health insurance had increased odds (AOR1.74; 95% CI 1.44‐2.11). Smoking was strongly associated with renal disease (AOR 3.07, 95% CI 2.38‐3.96). Other important associated factors included comorbidities such as diabetes (AOR 1.51, 95% CI 1.12‐2.04), hypertension (AOR 2.27, 95% CI 1.67‐3.08), obesity (AOR 12.75, 95% CI 9.97‐16.30), hypercholesterolemia (AOR 1.93, 95% CI 1.43‐2.60), and heart disease (AOR 6.84, 95% CI 5.14‐9.10). Conclusions: This large, community-based cross-sectional study identifies a significant burden of renal disease among adults in Riyadh and highlights several modifiable risk factors that could be targeted in preventive health programs. The findings emphasize the importance of integrating renal health screening within PHCs and strengthening public health strategies addressing obesity, hypertension, and smoking. While the study relied on self-reported data without biomarker or clinical confirmation, potentially underestimating true prevalence and introducing misclassification bias, it provides a crucial population-level baseline that can guide resource allocation and inform the development of nationwide surveillance systems for early detection of renal disease.

Background Regular exercise is crucial for preventing chronic diseases. Saudi Arabia, undergoing rapid socioeconomic transformation, faces escalating rates of lifestyle-related illnesses, underscoring the urgent need to understand exercise participation among its residents. This study aims to identify key sociodemographic and health-related predictors of exercise engagement in this unique context, thereby informing targeted public health strategies. Methods This cross-sectional survey was conducted from March to July 2023 across 48 primary healthcare centers in Riyadh, Saudi Arabia, selected via multi-stage cluster sampling. A total of 14,239 adults (aged ≥ 18) completed an electronic, interview-based questionnaire. A rigorously validated and reliable questionnaire collected self-reported data on sociodemographic characteristics, health behaviors and comorbidities. Univariate and multivariate logistic regression were used to identify predictors of exercise participation, with a p-value < 0.05 considered statistically significant in the adjusted model. Results Overall, a substantial 60.7% of participants reported engaging in regular exercise. Male sex was found to be a strong predictor (AOR = 1.14, 95% CI: 1.06–1.22, P = 0.001). Employed individuals were more likely to exercise (AOR = 1.40, 95% CI: 1.30–1.51, P < 0.001) than unemployed Saudi residents. The strongest predictor was health insurance coverage (AOR = 1.61, 95% CI: 1.48–1.75, P < 0.001). Smoking (AOR = 5.42, 95% CI: 4.82–6.08, P < 0.001) and fast-food consumption (AOR = 10.76, 95% CI: 9.77–11.84, P < 0.001) were also associated with higher odds of exercise. Individuals with heart disease showed significantly higher odds of exercising (AOR = 1.85, 95% CI: 1.42–2.41, P < 0.001). Conclusion Our findings underscore the complex interplay of sociodemographic factors, health behaviors, and existing health conditions in shaping exercise participation in Saudi Arabia. The significant influence of male sex, employment, insurance coverage, and the intriguing relationships with smoking, fast-food consumption, and specific health conditions, provide crucial insights. These results highlight key areas for developing tailored public health strategies and interventions to enhance exercise participation in Saudi Arabia.

Migraine is a common neurological disorder that substantially impacts individuals’ health and quality of life. This study aimed to estimate the prevalence of migraine and identify factors associated with it among patients attending primary healthcare centers (PHCs) in Saudi Arabia. We conducted a cross-sectional study from March to July 2023, involving 14,239 participants from 48 PHCs within Health Cluster 2 in the Riyadh region of Saudi Arabia. We collected data on participants’ sociodemographic characteristics, behavioral risk factors, and existing comorbidities using a validated questionnaire. Migraine status was determined based on participants’ self-reported prior physician diagnosis of migraine. To identify factors independently associated with migraine, we used multivariable logistic regression analysis. The adjusted odds ratios (AORs) and their 95% confidence intervals (CIs) were calculated to show the strength of these predictors. The prevalence of migraine was 5.9%. In the multivariable analysis, males had significantly lower odds of migraine compared to females (AOR = 0.69, 95% CI [0.59, 0.80], P < .001). Individuals with insurance coverage had higher odds of migraine (AOR = 2.44, 95% CI [2.11, 2.82], P < .001). Smoking (AOR = 2.44, 95% CI [2.05, 2.92], P < .001), and exercise (AOR = 1.46, 95% CI [1.18, 1.82], P < .001) were associated with higher odds of migraine. Several comorbidities were also significantly associated with migraine: obesity (AOR = 6.08, 95% CI [4.95, 7.47], P < .001), hypercholesterolemia (AOR = 2.59, 95% CI [2.07, 3.25], P < .001), hypertension (AOR = 1.47, 95% CI [1.15, 1.86], P = .002), and heart disease (AOR = 3.25, 95% CI [2.55, 4.15], P < .001). In this Saudi Arabian population, female sex, insurance coverage, smoking, obesity, hypercholesterolemia, hypertension, and heart disease were identified as significant predictors of migraine. These findings highlight the need for targeted preventive strategies and integrated care approaches addressing modifiable risk factors and comorbidities to reduce the burden of migraine.

Objective: This study aimed to explore the burden attributable to different groups of risk factors (environmental/occupational, behavioral, and metabolic) in Saudi Arabia that were stratified by gender and year and measured by summary exposure values (SEVs) and disability-adjusted life years (DALYs) per 100,000. Design: This study was structured as a systematic analysis. Methods: Using the GBD 2021 data, we extracted information on different risk factors attributed to the disease burden in Saudi Arabia to quantify the differences in exposure value (SEV) and disability-adjusted life year (DALY) rates (per 100,000) between females and males across different years. Results: Over the years, sustained progress in reducing the number of DALYs attributable to specific environmental and occupational risks has been observed, as well as a slight decrease in some behavioral risks. The highest disease burden was attributed to metabolic and behavioral risk factors, with body mass index being the leading risk factor for both genders. Between 1990 and 2021, the age-standardized DALY rate in those with high body mass indices increased by 168.4% and reached 3436.23 (95% UI 1878.7–5031.5) in males and increased by 125.2% to reach 2952.6 (95% UI 1456.9–4.407) in females. The age-standardized SEVs were the highest in females with a high body mass index, reaching an SEV of 57.98 (95% UI: 64.1–49.2), and in males, an SEV of 50.75 (95% UI: 57.1–42.3) was achieved. Regarding their attributable deaths in 2021, metabolic risk factors were identified as the primary contributors to NCD mortality in 2021. Conclusions: These results reveal persistent health disparities between males and females, underscoring the urgent need for gender-specific research, policies, and interventions. Strategies aimed at promoting health and reducing disease burden should acknowledge the unique health challenges encountered by males and females.

Background: Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field. Objective: This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care. Methods: We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest. Results: This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella. Conclusions: As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence’s impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.

Environmental health is one of the public health domains determined by physical, chemical, biological, social, and psychosocial factors. The current improvement and success in terms of technology, society, and the provision of multiple services after the Industrial Revolution were accompanied by a massive toll of air pollutants that were introduced and emitted into the air, causing global public health issues. It is crucial to understand how harmful air pollutants affect ecological health and the economic consequences of such effects. This study examined the relationship between Environmental Health assessed by CO Carbon Monoxide per capita (CO), Public Health Expenditures per capita (PHE), Toxic Air Pollution (PM2.5 and NO2), and Life Expectancy (LE), using the novel statistical model Bootstrap Autoregressive Distributed Lag (BARDL), for period data from 1990 to 2022 in Saudi Arabia. Results showed no cointegrating relationship and a negative one-way causal relationship between CO, NO2, PM2.5, and LE. Moreover, a positive two-way causal relationship was found between PHE and LE. Saudi Arabia initiated several strategies that aim to reduce environmental pollution and improve the overall quality of air, a step that will promote healthy living and have a vast positive economic impact in the long run.

Millions of people’s health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the “digital revolution,” characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.