Parental Happiness Socialization and Youth Adjustment in Italy and Azerbaijan in the COVID-19 Pandemic Era.

This study aims to cross-culturally identify the parental socialization strategies in response to a child's happiness and their associations with youth academic and socio-emotional adjustment, controlling for the impact of the COVID-19 pandemic. Participants were a convenient sample of Italian (N = 606, 81.9% mothers) and Azerbaijanis (N = 227, 61.4% mothers) parents of youths (Mage = 12.89, SD = 4.06; 51% girls). Parents filled out an online survey to assess their socialization strategies in response to their children's happiness, their children's negative emotion regulation and dysregulation, academic performance, and prosocial behavior. Exploratory factorial analysis showed the presence of two factors that enclosed supportive and unsupportive parental socialization strategies. A multiple-group path analysis model showed that similarly across countries, supportive parental strategies were positively related to youths' prosocial behavior and that unsupportive parental strategies were positively related to youths' negative emotion dysregulation, and negatively related to youths' academic performance and negative emotion regulation. Those results emerged controlling for parents' and adolescents' gender and age, parents' educational level, social desirability, and Covid-related problems. This study advances cross-cultural knowledge about the impact of the strategies that parents use to socialize their children's happiness in the unique context of the COVID-19 pandemic.

Nagorno-Karabakh conflict: Mental health repercussions and challenges in Azerbaijan.

The Armenia-Azerbaijan Nagorno-Karabakh conflict has made many Azerbaijanis internally displaced (IDP), missing, wounded, disabled, or killed. The war and its aftermath have drastically impacted the mental health of these populations, leading to depression, anxiety, and stress-related psychosomatic illnesses such as dissociative disorder. However, their access to medical and psychological services is limited. Intervention in terms of policy making, healthcare services, information systems, and social support is required to promoting social integration and facilitate accessibility to mental healthcare. National and international organisations, foreign aids, and media support can contribute to this end. The psychological effects of the war on children, healthcare shortage associated with COVID-19, accommodation and welfare problems, limited access to vaccination, technology, and educational opportunities, dangerous explosive remnants, as well as isolation and marginalisation of the IDPs can all be challenges facing the mental health of these populations. Organising programs such as support groups to share wartime experience, resilience promotion, psychosocial support for healthcare providers, religious identification, integral COVID-19 management, beside accommodation and education support are recommended to enhance the condition of these Azerbaijani populations.

Factors associated with early uptake of COVID-19 vaccination among healthcare workers in Azerbaijan, 2021.

INTRODUCTION: We evaluated uptake and factors associated with COVID-19 vaccination among health workers (HWs) in Azerbaijan. RESULTS: Among 1575 HWs, 73% had received at least one dose, and 67% received two doses; all received CoronaVac. Factors associated with vaccination uptake included no previous COVID-19 infection, older age, belief in the vaccine's safety, previous vaccination for influenza, having patient-facing roles and good or excellent health by self-assessment. CONCLUSION: These findings could inform strategies to increase vaccination uptake as the campaign continues.

Evaluation of epidemiology, clinical features, prognosis, diagnosis and treatment outcomes of patients with COVID-19 in West Azerbaijan Province.

BACKGROUND: COVID-19 is considered a widespread concern in global public health. Diagnoses of COVID-19 in some cases are necessary because of severe prognosis. In this study, epidemiologies, clinical and demographic characteristics of patients with COVID-19 were studied in Taleghani Hospital, Urmia, Iran. METHODS: This descriptive-analytical cross-sectional study was carried out on 215 patients with COVID-19 during March and April 2020. Approved COVID-19 case was considered as a person with a positive respiratory sample performed by at least one of two RT-PCR methods or genetic sequencing. ANOVA repeated measure, independent t-test and logistic regression were done. A P < .05 was considered significant. RESULTS: The mean age of patients was 50.93 ± 17.92 years. Regarding gender, there were 91 females (42.3%) and 124 males (57.7%). The mean hospital stay, the temperature at admission, and onset of symptoms were 4.91 ± 3.68 days, 37.40 ± 0.96°C and 5.88 ± 4.80 days, respectively. Close contact with suspected people was found in 10.2% of patients. Additionally, 44 patients (20.5%) were smokers. Shortness of breath and cough were found in 62.8% and 49.3% of patients. Diabetes mellitus and hypertension were the most common comorbidities of patients. Regarding lung involvement, 33 patients (33%) were normal, most of the patients (n = 71) had 5%-25% involvement in their lung and a minority of patients (n = 13) had a severe condition of 50%-75% lung involvement. The association between smoking and mortality was tested using chi-square showing no significant difference (X2 :2.959, P = .085). There was no significant difference between AST, ALT, ALP, total, direct Bilirubin, lung involvement and suffering from fever (P > .05). High Spo2 can increase the chance of recovery by 24% with each unit reduction. Kidney involvement increases the chance of death by about 80% (95% CI: 0.104-0.013). The odds ratio of spo2 for recovery of COVID-19 was 1.24 (95% CI: 1.014-1.528; P = .037). Kaletra with odds ratio of 31.960 had the most highest effect on recovery following COVID-19 (P = .043). CONCLUSION: COVID-19 involves different organs of the body with different severity. In the meantime, smoking was not a risk factor for the virus or associated with severe manifestations of the disease. Patients with high creatinine and CPK, pulmonary involvement above 25%, and hypoxemia had a higher mortality rate. Increase of Spo2 by 1% can improve the patients by 24%. The results indicated that Kaletra had the most highest effect on improvement following COVID-19.

Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis.

BACKGROUND: SARS-CoV-2, the virus that caused the global COVID-19 pandemic, has severely impacted Central Asia; in spring 2020, high numbers of cases and deaths were reported in this region. The second wave of the COVID-19 pandemic is currently breaching the borders of Central Asia. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions while obscuring shifts in the pandemic, increases in infection rates, and the persistence of the transmission of COVID-19. OBJECTIVE: The goal of this study is to provide enhanced surveillance metrics for SARS-CoV-2 transmission that account for weekly shifts in the pandemic, including speed, acceleration, jerk, and persistence, to better understand the risk of explosive growth in each country and which countries are managing the pandemic successfully. METHODS: Using a longitudinal trend analysis study design, we extracted 60 days of COVID-19-related data from public health registries. We used an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: COVID-19 transmission rates were tracked for the weeks of September 30 to October 6 and October 7-13, 2020, in Central Asia. The region averaged 11,730 new cases per day for the first week and 14,514 for the second week. Infection rates increased across the region from 4.74 per 100,000 persons to 5.66. Russia and Turkey had the highest 7-day moving averages in the region, with 9836 and 1469, respectively, for the week of October 6 and 12,501 and 1603, respectively, for the week of October 13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19; its infection rate of 13.73 for the week of October 6 quickly jumped to 25.19, the highest in the region, the following week. The region overall is experiencing increases in its 7-day moving average of new cases, infection, rate, and speed, with continued positive acceleration and no sign of a reversal in sight. CONCLUSIONS: The rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze the pandemic trajectory and control spread. Policy makers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia.

Presentation of a developed sub-epidemic model for estimation of the COVID-19 pandemic and assessment of travel-related risks in Iran.

The COVID-19 pandemic is one of the contagious diseases involving all the world in 2019-2020. Also, all people are concerned about the future of this catastrophe and how the continuous outbreak can be prevented. Some countries are not successful in controlling the outbreak; therefore, the incidence is observed in several peaks. In this paper, firstly single-peak SIR models are used for historical data. Regarding the SIR model, the termination time of the outbreak should have been in early June 2020. However, several peaks invalidate the results of single-peak models. Therefore, we should present a model to support pandemics with several extrema. In this paper, we presented the generalized logistic growth model (GLM) to estimate sub-epidemic waves of the COVID-19 outbreak in Iran. Therefore, the presented model simulated scenarios of two, three, and four waves in the observed incidence. In the second part of the paper, we assessed travel-related risk in inter-provincial travels in Iran. Moreover, the results of travel-related risk show that typical travel between Tehran and other sites exposed Isfahan, Gilan, Mazandaran, and West Azerbaijan in the higher risk of infection greater than 100 people per day. Therefore, controlling this movement can prevent great numbers of infection, remarkably.

Spatial and temporal analysis of the COVID-19 incidence pattern in Iran.

In the present paper, province-level variations of novel coronavirus (COVID-19) disease incidence across Iran were investigated. To this end, a geo-database from infected cases, deaths, total population, death-to-population ratio, and infected case-to-death ratio for 31 provinces of Iran and during seven successive periods of February 18-March 19 (P1), March 20-April 8 (P2), April 9-April 28 (P3), April 29-May 20 (P4), May 21-June 20 (P5), June 21-September 21 (P6), and September 22-October 21 (P7) of 2020 was built. Based on the last reports of the population and housing census (2018), Iran is home to 81.8 million people. Nationwide case series of 536,181 and 29,403 infected cases and deaths respectively with COVID-19 were reported to Iran's Minister of Health from February 18 to October 21, 2020. Of the infected cases, 5.48% have died. The spatiotemporal patterns of COVID-19 were different throughout the 31 study provinces. Firstly, the central, north, and northwest of Iran were the main hosts of this virus. Shortly after, other parts of Iran, most notably the west and southwest regions, experienced a momentous rise in the numeral of infected cases and deaths. In the first study period, Tehran, West Azerbaijan, Mazandaran, Qom, and Gilan had the most infected cases (> 1000). In the second to fourth periods, the number of provinces with higher than 1000 infected cases, respectively, reached 12, 10, and 17. For the last three periods (P5-P7), 31 provinces were assigned higher than 1000 infected cases. Tehran had the most deaths with an increasing trend for all study periods among other provinces. The areas around Kohgiluyeh and Boyer-Ahmad, Bushehr, Chaharmahal and Bakhtiari, South Khorasan, and Ilam had the least death cases. The numbers of infected (death) cases in Iran for the first to seventh periods (P1-P7) respectively were 16,730 (1208), 41,285 (1595), 28,530 (1876), 16,366 (1856), 80,694 (6588), 201,585 (6232), and 128,336 (6953). The location quotient (LQ) index showed that most provinces of Iran have the LQ > 1 indicating a high degree of COVID-19 concentration in most of the province's area in comparison with the nation, especially in the last study period (P7).