ABSTRACT
Purpose: This paper to identify those states that suffered the largest job losses, largest GDP declines and the highest unemployment rates and those states whose employment levels, unemployment rates and GDP declines were smallest during the COVID-19 recession. In addition, this paper endeavors to provide at least preliminary insights into why some states faired so poorly, whereas other states suffered so little during this downturn. Design/methodology/approach: This paper uses descriptive statistics and regression analysis to analyze the differences in state performance during the COVID-19 recession and recovery. Findings: The results from the two estimated regression models suggest that where you lived determined the severity of the recession and living in a blue state negatively impacted the strength of state's unemployment rate recovery. Research limitations/implications: This paper looks at only a two-year period starting with the COVID-19 recession and ending in December 2021. Practical implications: This paper provides a regional assessment of the COVID-19 recession and recovery on both a state and regional level. Social implications: The paper uses descriptive statistics to characterize the substantial state-level differences in the relative magnitude of economic decline due to the Covid-19 recession. Regression analysis reveals that blue states experienced weaker recovery as compared to red states. Originality/value: The study uses publicly available data to identify states that suffered the largest job losses and highest peak unemployment rates during the Covid-19 recession. The results are among the first to analyze the economic impact of the Covid-19 recession at the state level. © 2022, Emerald Group Publishing Limited.
ABSTRACT
Purpose: This paper to identify those states that suffered the largest job losses, largest GDP declines and the highest unemployment rates and those states whose employment levels, unemployment rates and GDP declines were smallest during the COVID-19 recession. In addition, this paper endeavors to provide at least preliminary insights into why some states faired so poorly, whereas other states suffered so little during this downturn. Design/methodology/approach: This paper uses descriptive statistics and regression analysis to analyze the differences in state performance during the COVID-19 recession and recovery. Findings: The results from the two estimated regression models suggest that where you lived determined the severity of the recession and living in a blue state negatively impacted the strength of state's unemployment rate recovery. Research limitations/implications: This paper looks at only a two-year period starting with the COVID-19 recession and ending in December 2021. Practical implications: This paper provides a regional assessment of the COVID-19 recession and recovery on both a state and regional level. Social implications: The paper uses descriptive statistics to characterize the substantial state-level differences in the relative magnitude of economic decline due to the Covid-19 recession. Regression analysis reveals that blue states experienced weaker recovery as compared to red states. Originality/value: The study uses publicly available data to identify states that suffered the largest job losses and highest peak unemployment rates during the Covid-19 recession. The results are among the first to analyze the economic impact of the Covid-19 recession at the state level. © 2022, Emerald Group Publishing Limited.
ABSTRACT
Background: During the CoViD-19 pandemic in type 2 diabetic patients (T2DM pts) present in our Medicine wards we administered a big amount of s.c. insulin through the basal bolus regimen in order to obtain a good metabolic control;often difficult to reach both for the septic state and steroid therapy. Since we administered almost 60 to 80 units/insulin per day (TDD), not always obtaining a good metabolic control, we thought to switch some of our in-patients treated with basal bolus insulin regimen to a bolus plus a fixed ratio combination of GLP1-RA/basal insulin trying to take advantage of both the antiinflammatory and slimming effect of GLP1-RA. Results: Between October and December 2020 we evaluated 54 T2DM in-patients with CoViD-19 pneumonia.They were 41 males and 13 females aged 70±5 yrs, BMI 31±3kg/sm with an average HbA1c 8.3±1.2 and an insulin TDD of 70±11 s.c. compared to 40 matched (for sex, age, BMI and insulin TDD) control pts treated with basal bolus regimen. Conclusions:We detected in the fixed ratio combination of GLP1- RA/basal insulin a faster and stable good metabolic control vs the basal bolus treated pts. We reduced the average amount of insulin TDD of 34% with great satisfaction of pts and nurses.
ABSTRACT
INTRODUCTION SARS-CoV-2 pandemic has having brought about profound changes in the socio-health sector, especially in the provision of the main levels of hospital and territorial assistance, with consequences of public health. To date, the management path of pregnant women, CoV-2 affected, and of their newborns is also well defined: in the absence of defined risk factors and/or in presence of good general clinical conditions of the woman, the completion of the pregnancy with caesarean section is not recommended, such as preterm labor (<37 weeks) is not justified in the absence of ascertained conditions that could endanger the pregnant or the fetus. Vertical transmission of the new coronavirus infection (intrauterine, intrapartum or peripartum) is very rare, and postnatal horizontal infection is more likely. In the event that a newborn were to become infected, most of them are asymptomatic and do not require ventilatory support;the main risk factors for severe disease are being preterm and having severe underlying medical conditions. Since the likelihood of having a SARS-CoV-2 positive rhino-pharyngeal swab is similar between infants separated from their mother at birth, compared to those in rooming-in (where preventive measures are implemented), current guidelines of the Italian Society of Neonatology approve the maintenance of the mother-child dyad, unless the mother is unable to care for her newborn. UOC of Neonatology with NICU of 'Vincenzo Cervello' hospital in Palermo, responsible for the management of COVID-19 positive births in Western Sicily, has conducted a study to identify how many children were born from CoV-2 affected mothers, the number of vertically infected newborns, the number of preterm births, underling how these data relate to the epidemiological curve of infection. METHODS This cross-sectional study was conducted over a period of 12 months, from March 2020 to March 2021, plotting the data on a histogram. The percentages of preterm births and positive newborns in the first 48-72 h of life (likely vertical transmission) were also calculated. RESULTS The percentage of preterm births (about 7.4%) appears to be slightly lower than the average of the national and international data (10%), probably linked to the small number of the sample, the main limitation of this study. The rarity of vertical transmission is evidenced by the only case of a positive birth from a positive mother (0.8% of the total). The greatest number of related COVID-19 births took place on the 19th of November 2020, when there was a peak of 1837 cases of new infected in a single day, with over a third of cases recorded in the provinces of Palermo and Trapani (Figure 1). The second peak has occurred on the 15th of January 2021, with 1945 new cases of infected people without a concomitant trend in pregnant women. CONCLUSIONS From the results obtained it is evident that the births-COVID curve partially follows the local epidemiological one (Figure 2): many factors can influence the epidemiological curve's trend such as the basic seasonality in births. Further studies are necessary to underline if a real correlation is present or not.