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Workforce dynamics and flattening the curve

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Via VOX EU

Informed by early epidemiological modelling (Anderson et al. 2020), current efforts to fight the COVID-19 pandemic revolve around the goal of ‘flattening’ the curve of infections to keep within the capacity of health care systems to treat severe cases (Baldwin 2020, Baldwin and di Mauro 2020). Physical distancing is the main strategy to achieve this goal, and currently, more than half of the world’s population has been asked or ordered by their governments to stay at home to limit the spread of COVID-19.

During the past four weeks, as the virus quickly spread around the globe and infected more than one million people, the daily pandemic statistics have been scoured for evidence to show that the strategies to limit transmission are effective. The main indicator used is the daily increase in the number of new infections, which was chosen for early transmission models of COVID-19 simulating the flattening of the curve (Anderson et al. 2020).

Although it is well understood that COVID-19 severity varies with a person’s age (Novel Coronavirus Pneumonia Emergency Response Epidemiology Team 2020, Verity et al. 2020, Wu et al. 2020), little consideration has yet been given to the variation in infection trends across age groups (Bignami-Van Assche and Ghio 2020). This is an important omission, as the Italian experience illustrates.

After three tragic weeks of sustained increases, the daily number of new COVID-19-related infections and deaths in Italy has, during the past few days, shown a declining trend (Italian Ministry of Health 2020). This suggests that the peak of the outbreak has been reached and has prompted discussions about when and how the lockdown measures should be relaxed.

However, the levelling off of COVID-19-related infections is not uniform across age groups. In Table 1, we present the age distribution of the cumulative number of confirmed positive cases, which the Italian Institute of Health (Istituto Superiore di Sanità, or ISS) has been publishing bi-weekly since March 12. Throughout the observation period, more than 80% of the confirmed cases were concentrated between the age groups of 40–49 and 80–89 years.

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The age groups 60–69 and 70–79 years saw their shares of cumulative number of infections decrease by 4% and 2%, respectively, within the observation period. A different trend is observed among the middle age groups of working-age individuals (40–49 and 50–59 years). For these two groups, the proportion of cases increased in the early periods before stabilising after March 26, which is consistent with the adoptions of containment measures for working activities implemented by the Italian government as of March 25.

Table 1 Percentage distribution of the cumulative number of COVID-19 confirmed positive cases, by age groups (Italy, March 12 – April 6)

Source: Italian Health Institute (Istituto Superiore di Sanità, ISS).

The different pace of infections by age can be seen by plotting the distribution of the cumulative number of cases for the four most affected age groups. Figure 1 shows that the declining trend in the total number of cases has been driven mainly by a declining slope of the curve for the age groups 60–69 and 70–79 years. There is no evidence of slope flattening for the working-age categories 40–49 and 50–59 years and the elderly (80–89 years old). Importantly, since mid-March, the largest number of confirmed cases is found among 50–59 years old.

Figure 1 Cumulative number of COVID-19 confirmed positive cases by age group (Italy, March 12 – April 6)

Source: Italian Health Institute (Istituto Superiore di Sanità, ISS).

These findings show how the national lockdown enforced on March 8 led to different degrees of physical distancing along the working-age divide. People above 60, and especially above the statutory retirement age of 67 years, substantially reduced their rate of contact with others since they were mostly excluded from essential work activities. In contrast, a substantial proportion of working-age individuals in the essential sectors (including healthcare workers) had to continue to leave their house. After the national lockdown, the workforce demographics thus helped reduce the exposure to COVID-19 for the elderly disproportionately more than for the working-age population.

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The issue is that the working-age divide does not fully align with the age at which COVID-19 severity increases. Figure 2 shows that the age-specific case fatality risk for COVID-19 (the ratio between deaths and confirmed positive cases in each age group, or CFR) in Italy begins to increase at age 50, as it is also found in studies on Wuhan (Verity et al. 2020, Wu et al. 2020). Although CFR is smaller between ages 50 and 69, and then above age 70, the CFR for the working-age population on April 6 translated into 16% of all recorded deaths in the 50–69 group. This is why the high concentration of confirmed cases in the age groups 50–59 and 60–69 years and the unabated rise especially in the former category are worrisome trends that should be monitored closely.

Figure 2 Age-specific CFR for COVID-19 confirmed positive cases (Italy, April 6)

Source: Italian Health Institute (Istituto Superiore di Sanità, ISS).

These considerations suggest that the effectiveness of strategies aimed at ‘flattening the curve’ of COVID-19 infections crucially depends on whether the labour market measures integrate age-group profiling. Since the share of jobs that can be performed without putting workers’ health at risk is limited (Boeri et al. 2020), restricting the age of essential workers can be a useful policy to mitigate the work-security trade-off while keeping the economy going. Governments should also carefully consider the demographics of COVID-19-related infections when planning how to gradually lift lockdown measures and reopen the economy.

References

Anderson, R M, H Heesterbeek, D Klinkenberg and T D Hollingsworth (2020), “How will country-based mitigation measures influence the course of the COVID-19 pandemic?” The Lancet, 395(10228): 931–4.

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Baldwin, R (2020), “It’s not exponential: an economist’s view of the epidemiological curve“,VoxEU.org, March 12.

Baldwin, R, and di Mauro, BW. (2020). Mitigating the COVID economic crisis: Act fast and do whatever it takes, VoxEU.org eBook, CEPR Press.

Bignami-Van Assche, S, and D Ghio (2020), “A demographic adjustment to improve measurement of COVID-19 severity at the developing stage of the pandemic”, medRxiv.

Boeri T, A Caiumi and M Paccagnella (2020), “Mitigating the work-security trade-off while rebooting the economy“, VoxEU.org, April 9.

Italian Ministry of Health (2020). Covid-19: Situazione in Italia.

Italian Health Institute [Istituto Superiore di Sanità] (2020). Sorveglianza Integrata COVID-19: i principali dati nazionali.

Novel Coronavirus Pneumonia Emergency Response Epidemiology Team (2020), “The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China”, Chinese Center for Disease Control and Prevention Weekly 41: 145–51.

Verity T, et al. (2020), “Estimates of the severity of coronavirus disease 2019: a model-based analysis”, The Lancet, March 30.

Wu, J T, K Leung, M Bushman, et al. (2020), “Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China”, Nature Medicine 26: 506–10.


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