Objective: This study aimed to address the key areas of concern for child nutrition, both during and after the COVID-19 pandemic, and proposes strategic responses to reduce child undernutrition in the short and long term. Design: A descriptive literature review was performed. The search of the literature was conducted through using electronic databases including PubMed, Web of science, google scholar, and Cochrane library. Setting: A wide range of published articles focused on child malnutrition were reviewed. Participants: The study was focused on children especially those under five years. Results: This study proposes strategic responses to reduce child undernutrition. These responses include strengthening access to community-based nutrition services that support the early detection and treatment of undernourished children and emergency food distribution, including fortified foods with vitamins and minerals, to vulnerable households, particularly those with children under five years. Moreover, counseling and promotion programs should be reinforced to revitalize community nutrition education in areas such as gestation, exclusive breastfeeding, and complementary feeding, and hygienic practices involving handwashing, proper sanitation, and other basic behavioral changes. Conclusions: The COVID-19 pandemic has affected many countries especially those in the regions of South Asia and sub-Saharan Africa in which there has been an ongoing burden of child undernutrition. However, malnutrition is preventable and can be eliminated through a multisectoral strategic approach. The effective execution of a multisectoral approach toward preventing childhood malnutrition will require not only a financial investment but also the collective efforts from different ministries of the governments, UN-affiliated agencies, and nongovernmental organizations.

BackgroundAlthough some studies have highlighted short birth interval as a risk factor for adverse child nutrition outcomes, the question of whether and to what extent long birth interval affects better nutritional outcomes in children remains unclear.MethodsIn this quantitative meta-analysis, we evaluate the relationship between different birth interval groups and child nutrition outcomes, including underweight, wasting, and stunting.ResultsForty-six studies with a total of 898,860 children were included in the study. Compared with a short birth interval of <24 months, birth interval of ≥24 months and risk of being underweight showed a U-shape that the optimum birth interval group of 36–48 months yielded the most protective effect (OR = 0.54, 95% CI = 0.32–0.89). Moreover, a birth interval of ≥24 months was significantly associated with decreased risk of stunting (OR = 0.61, 95% CI = 0.55–0.67) and wasting (OR = 0.63, 95%CI = 0.50–0.79) when compared with the birth interval of <24 months.ConclusionThe findings of this study show that longer birth intervals (≥24 months) are significantly associated with decreased risk of childhood undernutrition and that an optimum birth interval of 36–48 months might be appropriate to reduce the prevalence of poor nutritional outcomes in children, especially underweight. This information would be useful to government policymakers and development partners in maternal and child health programs, especially those involved in family planning and childhood nutritional programs.

Background and objectives: The COVID-19 pandemic continues worldwide, and there is no effective treatment to treat it. Chinese medicine is considered the recommended treatment for COVID-19 in China. This study aimed to examine the effectiveness of tetrandrine in treating COVID-19, which is originally derived from Chinese medicine. Materials and Methods: A total of 60 patients, categorized into three types (mild, moderate, severe), from Daye Hospital of Chinese Medicine with a diagnosis of COVID-19 were included in this study. Demographics, medical history, treatment, and results were collected. We defined two main groups according to the clinical outcome between improvement and recovery. All underlying factors including clinical outcomes were assessed in the total number of COVID-19 patients and moderate-type patients. Results: In a total of 60 patients, there were significant differences in the clinical outcome underlying treatment with antibiotics, tetrandrine, and arbidol (p < 0.05). When the comparison was limited to the moderate type, treatment with tetrandrine further increased recovery rate (p = 0.007). However, the difference disappeared, and no association was indicated between the clinical outcome and the treatment with and without antibiotic (p = 0.224) and arbidol (p = 0.318) in the moderate-type patients. In all-type and moderate-type patients, tetrandrine improved the rate of improvement in cough and fatigue on day 7 (p < 0.05). Conclusions: Tetrandrine may improve clinical outcome in COVID-19 patientsand could be a promising potential natural antiviral agent for the prevention and treatment of COVID-19.

Objective The annual influenza epidemic is a heavy burden on the health care system, and has increasingly become a major public health problem in some areas, such as Hong Kong (China). Therefore, based on a variety of machine learning methods, and considering the seasonal influenza in Hong Kong, the study aims to establish a Combinatorial Judgment Classifier (CJC) model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning. Methods The characteristic variables were selected using the single-factor statistical method to establish the influencing factor system of an influenza outbreak. On this basis, the CJC model was proposed to provide an early warning for an influenza outbreak. The characteristic variables in the final model included atmospheric pressure, absolute maximum temperature, mean temperature, absolute minimum temperature, mean dew point temperature, the number of positive detections of seasonal influenza viruses, the positive percentage among all respiratory specimens, and the admission rates in public hospitals with a principal diagnosis of influenza. Results The accuracy of the CJC model for the influenza outbreak trend reached 96.47%, the sensitivity and specificity change rates of this model were lower than those of other models. Hence, the CJC model has a more stable prediction performance. In the present study, the epidemic situation and meteorological data of Hong Kong in recent years were used as the research objects for the construction of the model index system, and a lag correlation was found between the influencing factors and influenza outbreak. However, some potential risk factors, such as geographical nature and human factors, were not incorporated, which ideally affected the prediction performance to some extent. Conclusion In general, the CJC model exhibits a statistically better performance, when compared to some classical early warning algorithms, such as Support Vector Machine, Discriminant Analysis, and Ensemble Classfiers, which improves the performance of the early warning of seasonal influenza.

BackgroundThe COVID-19 pandemic has continued to be a public health emergency currently; on March 11, 2020, the World Health Organization (WHO) declared it a global pandemic. Despite the Rwanda National Health Measures that have been put in place to protect the public including lockdowns, curfew, face mask mandate, handwashing sensitization, etc., severe morbidity and mortality cases of COVID-19 are continued to be seen. Some studies have linked COVID-19 complications to its direct chain of mechanism; however, other studies have linked comorbidity or underlying disease conditions to its poor prognosis. Studies have not yet been conducted in Rwanda on the severe status of COVID-19 and its associated factors among patients. Therefore, this study aimed to assess the severe status of COVID-19 and its associated factors at the Nyarugenge Treatment Center. MethodsA descriptive cross-sectional study was done. All patients admitted to the Nyarugenge Treatment Center from January 8, 2021, when the hospital opened, until the end of May 2021 were recruited in the study. The eligible participants were all patients who were admitted and tested positive for COVID-19 by RT-PCR method according to the Rwanda Ministry of Health criteria. ResultsAll data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 25 (IBM Corp., Armonk, NY). The number of patients admitted during the study period was 648, with a median age of 53; 45.2% of them were females, and 54.2% were males. Of these, 81.2% (526) were discharged from the hospital, while 18.8% (122) died. The proportion of severe status of COVID-19 was 42.1%. The factors that showed a risk of severe COVID-19 status were age and the number of comorbidities. Patients aged above 60 years (OR = 11.7, and those between the age of 51 and 60 (OR = 6.86, 95% CI: 2.96-15.93, p-value < 0.001) were 12 and seven times more likely to have severe COVID-19 status compared to those aged below 30 years. Having two comorbidities had twice the risk of developing a severe COVID-19 status compared to those with no comorbidity (OR = 2.13, 95% CI: 1.20-3.77, p-value < 0.001). ConclusionElderly people and those with comorbidities are encouraged to obtain all standard operating procedures and comply with the vaccination program.