Saliva is a useful biomarker for diagnosing oral health conditions, including periodontal disease (PD). Smoking is a risk factor for PD. The aim of this systematic review was to summarize the salivary biomarkers associated with PD based on smoking status. A comprehensive search of the MEDLINE (via PubMed), EMBASE, Cochrane, SCOPUS, and Web of Sciences databases was conducted up to 1 January 2021 using key terms relevant to the topic of our research and Cochrane methodology and improved with searching a gray literature resource. The methodological quality of all included studies was assessed with the revised Quality Assessment of Diagnostic Accuracy Studies-2. Seven studies were included. Smokers had increased levels of malondialdehyde, sialic acid, salivary cortisol, salivary interleukin 1β, albumin, tissue inhibitor of matrix metalloproteinase (TIMP), and the pyridinoline cross-linked carboxyterminal telopeptide of type I collagen (ICTP), as well as decreased levels of superoxide dismutase, activity of lactate dehydrogenase, activity of enzyme activity of β-glucuronidase, uric acid, matrix metalloproteinase-8 (MMP-8)/TIMP-1 ratio, and combinations of MMP-8 and ICTP. However, mixed results were observed some studies in detecting glutathione peroxidase, MMP-8, and MMP-14. The results were interpreted with caution because of limitations in the number of included studies and the study design. Some salivary biomarkers are potentially useful in combination or alone for diagnosing PD. Methodological and systematic studies are needed to develop more effective biomarkers.

Despite recent progress in Afghanistan’s health system from the support of international donors and NGOs, protracted conflicts combined with a series of natural disasters have continued to present substantial health risks. Extreme poverty has still aggravated social determinants of health and financial barriers to healthcare. Little is known about the context-specific factors influencing access to healthcare in the crisis-affected population. Using a subset of data from ‘Whole of Afghanistan Assessment (WoAA) 2019’, this study analyzed 31,343 households’ data, which was collected between 17 July and 19 September 2019 throughout all 34 provinces in Afghanistan. The outcome measured was access to care in the healthcare facility, and multivariable binary logistic regression models were used to identify the specific factors associated with access to healthcare. Of 31,343 households exposed to complex emergencies in Afghanistan, 10,057 (32.1%) could not access healthcare facilities when one was needed in last three months. The access to healthcare was significantly associated with displacement status, economic factors such as employment status or total monthly income, and the distance to healthcare facilities. Significant increase in healthcare access was associated with factors related to communication and access to information, such as awareness of humanitarian assistance availability and mobile phone with a SIM card, while disability in cognitive function, such as memory or concentration, was associated with poorer healthcare access. Our findings indicate that the crisis-affected population remains vulnerable in access to healthcare, despite the recent improvements in health sectors. Digital determinants, such as access to mobile phone, need to be addressed along with the healthcare barriers related to poverty and household vulnerabilities. The innovative humanitarian financing system using mobile communication and cash transfer programs would be considerable for the conflict-affected but digitally connected population in Afghanistan.

It is important to understand the ultimate control of COVID-19 in all countries around the world in relation to the characteristics of developed countries, LDCs, and the variety of transmission characteristics of COVID-19. Therefore, this study aimed to identify factors associated with confirmed cases of COVID-19 with a focus on the Human Development Index (HDI). The units of analysis used for the current study were countries, and dataset were aggregated from multiple sources. This study used COVID-19 data from Our World in Data, the Global Health Security Index, and the WORLD BANK. A total of 171 countries were included in the analysis. A multi-variable linear regression with a hierarchical framework was employed to investigate whether the HDI is associated with confirmed COVID-19 cases after controlling for the demographic and healthcare system characteristics of the study countries. For Model 2, which controlled for demographic and healthcare system characteristics, HDI (β = 0.46, p < 0.001, 95% CI = 2.64–10.87) and the number of physicians per 1000 people (β = 0.34, p < 0.01, 95% CI = 0.21–0.75) had significant associations with the total number of confirmed COVID-19 cases per million people. Countries with a high HDI level are able to conduct higher per capita testing, resulting in higher numbers of confirmed cases than in countries with lower HDI levels. This study has shown evidence that could be used by governments and international organizations to identify national characteristics and provide the international cooperation necessary to develop effective prevention and intervention methods to deal with the global pandemic.