Unemployment and widespread influenza in America, 1999-2010

Change in unemployment by social vulnerability among United States counties with rapid increases in COVID-19 incidence—July 1–October 31, 2020

Objective

During the COVID-19 pandemic, the unemployment rate in the United States peaked at 14.8% in April 2020. We examined patterns in unemployment following this peak in counties with rapid increases in COVID-19 incidence.

Method

We used CDC aggregate county data to identify counties with rapid increases in COVID-19 incidence (rapid riser counties) during July 1–October 31, 2020. We used a linear regression model with fixed effect to calculate the change of unemployment rate difference in these counties, stratified by the county’s social vulnerability (an indicator compiled by CDC) in the two months before the rapid riser index month compared to the index month plus one month after the index month.

Results

Among the 585 (19% of U.S. counties) rapid riser counties identified, the unemployment rate gap between the most and least socially vulnerable counties widened by 0.40 percentage point (p<0.01) after experiencing a rapid rise in COVID-19 incidence. Driving the gap were counties with lower socioeconomic status, with a higher percentage of people in racial and ethnic minority groups, and with limited English proficiency.

Conclusion

The widened unemployment gap after COVID-19 incidence rapid rise between the most and least socially vulnerable counties suggests that it may take longer for socially and economically disadvantaged communities to recover. Loss of income and benefits due to unemployment could hinder behaviors that prevent spread of COVID-19 (e.g., seeking healthcare) and could impede response efforts including testing and vaccination. Addressing the social needs within these vulnerable communities could help support public health response measures.

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture

BACKGROUND

Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano.

METHODS

Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring.

RESULTS

In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down.

HYPOTHESIS AND DISCUSSION

The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

The effects of employment on influenza rates

The seasonal influenza virus afflicts millions of people in the U.S. population each year, imposing significant costs on those who fall ill, their families, employers, and the health care system. The flu is transmitted via droplet spread or close contact, and certain environments, such as schools or offices, promote transmission. In this paper, we examine whether increases in employment are associated with increased incidence of the flu. We use state-level data on the prevalence of the flu from the Centers for Disease Control and Prevention. In our preferred specification, we find that a one percentage point increase in the employment rate increases the number of influenza related outpatient health care visits by 19%, and these effects are highly pronounced in the retail sector and healthcare sector, the sectors with the highest levels of interpersonal contact.

Impact of medical and behavioural factors on influenza-like illness, healthcare-seeking, and antiviral treatment during the 2009 H1N1 pandemic: USA, 2009-2010

We analysed a cross-sectional telephone survey of U.S. adults to assess the impact of selected characteristics on healthcare-seeking behaviours and treatment practices of people with influenza-like illness (ILI) from September 2009 to March 2010. Of 216,431 respondents, 8.1% reported ILI. After adjusting for selected characteristics, respondents aged 18-64 years with the following factors were more likely to report ILI: a diagnosis of asthma [adjusted odds ratio (aOR) 1.88, 95% CI 1.67-2.13] or heart disease (aOR 1.41, 95% CI 1.17-1.70), being disabled (aOR 1.75, 95% CI 1.57-1.96), and reporting financial barriers to healthcare access (aOR 1.63, 95% CI 1.45-1.82). Similar associations were seen in respondents aged ≥ 65 years. Forty percent of respondents with ILI sought healthcare, and 14% who sought healthcare reported receiving influenza antiviral treatment. Treatment was not more frequent in patients with high-risk conditions, except those aged 18-64 years with heart disease (aOR 1.90, 95% CI 1.03-3.51). Of patients at high risk for influenza complications, self-reported ILI was greater but receipt of antiviral treatment was not, despite guidelines recommending their use in this population.

Bad Jobs, Bad Health? How Work and Working Conditions Contribute to Health Disparities

In this review, we touch on a broad array of ways that work is linked to health and health disparities for individuals and societies. First focusing on the health of individuals, we discuss the health differences between those who do and do not work for pay, and review key positive and negative exposures that can generate health disparities among the employed. These include both psychosocial factors like the benefits of a high status job or the burden of perceived job insecurity, as well as physical exposures to dangerous working conditions like asbestos or rotating shift work. We also provide a discussion of the ways differential exposure to these aspects of work contributes to social disparities in health within and across generations. Analytic complexities in assessing the link between work and health for individuals, such as health selection, are also discussed. We then touch on several contextual level associations between work and the health of populations, discussing the importance of the occupational structure in a given society, the policy environment that prevails there, and the oscillations of the macroeconomy for generating societal disparities in health. We close with a discussion of four areas and associated recommendations that draw on this corpus of knowledge but would push the research on work, health and inequality toward even greater scholarly and policy relevance.