Jianhong Wu, Biao Tang, Yanni Xiao, Sanyi Tang, Aria Ahmad, James Orbinski, York University, Xi’an Jiaotong University, and Shaanxi Normal University

Contact: orbinski@yorku.ca

All Countries are Attempting to Mitigate COVID-19, but with Varying Success

The ongoing COVID-19 pandemic has led to unprecedented mitigation policies designed to identify, contain, control, and prevent outbreaks of SARS-CoV-2 infection. Different jurisdictions have taken different approaches, with varying success. This article contrasts experience with mitigating the pandemic in China and in Ontario, Canada. We suggest strategies that can mitigate the pandemic more quickly and effectively.

Experience in China

Mainland China has achieved successful rapid responses to COVID-19 outbreaks, beginning with the complete lockdown of the first reported outbreak epicenter of Wuhan on January 23, 2020. By February 16, 2020, a month-long, nation-wide lockdown was expanded alongside the four-part “All Policy” – Quarantine All, Collect All, Detect All, Treat All.¹ Collectively, these public health measures contributed to mainland China reporting no new cases by March 19, 2020. Following subsequent reopening, local outbreaks in Beijing, Xinjiang and Dalian were reported on June 11, July 7, and July 22, 2020 respectively. Swift lockdowns of provinces with affected cities coupled with expanded contact tracing, access to treatment, and rapid population-wide testing in areas with potential exposure to infection contributed to the success of no new cases in over a month, as shown in Figure 1(A).

When three cases of COVID-19 were confirmed on October 11, 2020 in Qingdao, the city was locked down. The governments’ pre-set scenario goal of immediate contact tracing and immediate testing of the entire Qingdao population (9 million) in five days was exceeded, as more than 10 million samples were collected and tested within four days. With 13 positive cases identified, the city was thereafter reopened. These “swift mitigation’” measures – rapidly reducing contacts followed by intensive contact tracing and widespread population testing —characterize the success of treatment, containment, and prevention in mainland China. From the peak to end of the first and -thus far- only wave of COVID-19 in the country, it took about 45 days. While this specific approach may not be feasible in all regions globally, the experience suggests key elements of a critical problem-solving strategy for subsequent waves of population transmission. Successful approaches need to invoke swift, focused and coherent mitigation interventions that are likely to be more effective than those that are piece-meal, poorly coordinated, and/or ill-timed.

Experience in Ontario, Canada

Modeling social distancing interventions: Initial reductions with risk of recurrence

A similar set of mitigation strategies was employed by governments around the world in their initial response to the first wave of SARS-CoV-2.^2,3,4. Taking the Canadian province of Ontario as an example, we modelled a series of escalating social distancing mitigation interventions that were implemented in three consecutive stages between March 14 and May 16, 2020, followed by staggered de-escalation through to September 21, 2020 (Figure 1B). In addition to school closures (Stage 1) and restrictions to public events and recreational venues (Stage 2), the “swift mitigation” measures of shutting down all non-essential workplaces (Stage 3) was essential to reducing the Effective Reproduction Number,  below the 1.0 threshold by on or around April 16, 2020.

This trend marked a strong start but was still susceptible to the emergency of new infections. Reflecting the sum effect of all mitigation interventions, when  approaches the 1.0 threshold and the number of infections in the population is significant, a subsequent wave of infection is likely to emerge. A new wave is particularly likely where increased social contact allows for broader-based transmission, thereby driving  above the 1.0 threshold.

Recurrence: April to September 2020

Using a transmission dynamics model fitted to the daily incidence data in Ontario (Figure 1C), we observed two key trends. First, although  decreased to below 1.0 around April 16, 2020, it remained close to this threshold, resulting in only a slow case decline rate. Second, even with the quarantine proportion and case detection rate achieved at the end of Stage 3,  can exceed the 1.0 threshold when the contact rate reaches only two-thirds of the normal full rate after reopening. This indeed happened when the province moved to regional de-escalation from Stage 3 on July 16, 2020 allowing businesses and public places in approved regions to re-open with safety and occupational measures in place.

Our estimation shows a gradual increase in the contact rate (Fig. 1E). However, contact tracing coverage and testing capacity remained relatively unchanged, with the quarantine proportion at 38% and a diagnostic testing confirmation period of 7.5 days after infection. As a result, the effective reproduction number gradually increased to 1.5, leading to a second wave in September 2020. In turn, the Province re-imposed modified Stage-2 mitigation measures in some hotspots.

Simulations: What if contact tracing had been higher and testing faster?

Using our model, we simulated a May 16, 2020 onset in Ontario of an increased quarantine proportion from 40% to 70% and a case detection period shortened from 7.5 to 4 days (Figure 1F). With this enhanced testing and contact tracing capacity,  would have dropped from 0.78 to 0.30 at the time of Stage 1 reopening. The accelerated rate of case decline across the Province would have ended the first outbreak by the end of June 2020, with a reopening that would have started with a significantly lower number of undetected infections (if any). This would have allowed public health resources to be mobilized for swift and focused responses to any new localized hotspots, and likely avoiding a full-scale second wave of transmission.

In contrast to these desirable cascade effects – where enhanced social distancing measures maintain a low leading to reduced number of new infections, together with swift public health testing and contact tracing – Ontario experienced the undesirable cascade effects of pre-mature relaxation of social distancing. Unfortunately, as a result, the exponential growth from a large initial pool of undetected infections quickly led to outbreaks exceeding testing and tracing capacity. This negative cascade reinforced the resurgence of the pandemic in Ontario.

The power of tipping points: Triggers for a second wave in Ontario?

Stage 1 reopening in Ontario started on May 16, 2020 even as new cases continued to be reported daily, suggesting that  may have been hovering around the 1.0 threshold. According to catastrophe theory and dynamic systems bifurcation theory, the province was at a “bifurcation point” with subsequent tipping point cascade effects, when de-escalation of social distancing began on May 16, 2020.

Tipping point cascade effects suggest that while a small increase to the bifurcation parameter (in this case,  can trigger a second wave, further reduction of this bifurcation parameter near the tipping point can accelerate an exponential decline in new case transmissions. In Ontario, the number of total and new daily cases was judged to be “small” — wrongly, it appears — in deciding to begin reopening on May 16, 2020. Public health resources were likewise judged to be sufficient, instead of scaling testing, contact tracing, quarantine, and isolation efforts, which would lead to a further decrease of . Hence, the analysis suggests a missed opportunity to prevent the second wave.

Implications for Global Mitigation During the Second Wave

Similar situations globally can be identified on the WHO Coronavirus Disease Dashboard, with countries reporting subsequent waves of transmission with even larger peaks that have imposed significant challenges to public health systems in the face of public fatigue.⁵ Prolonged lockdowns have become a real possibility in many parts of the world. Yet this time, a critical problem-solving strategy for subsequent waves of COVID-19 population transmission should invoke swift, focused, and coherent mitigation interventions that aim to reduce the effective reproduction number well below the 1.0 threshold. This approach is likely to be more effective than strategies that are piece-meal, poorly coordinated, and ill-timed.

At least two factors have contributed to the occurrence of second waves in many countries. First, SARS-CoV-2 has high transmissibility with a high basic reproduction rate when activities resume.^6,7 Second, although most countries implemented a range of public health mitigation interventions, implementation has often been lax and inconsistent, resulting in tipping point cascade effects that lead to further outbreaks.

One important strategy for controlling infectious disease outbreaks is to reduce below the 1.0 threshold. However, it is easy to stop too soon. Given the high transmissibility and reproduction rate of SARS-CoV-2, an effective reproduction number below but near this threshold could result in a prolonged period until the end of the outbreak.

Looking Forward

The global scale of the COVID-19 pandemic has catalyzed to significant advancements in public health interventions, including rapid testing and enhanced contact tracing capacities. With these, the period from peak to end of the second wave can and should be shortened when tipping point cascade effects are fully taken into account.

Effective strategies to confront subsequent waves of outbreaks and to safely reopen from full, staged or localized lockdowns require swift, focused, and coherent mitigation interventions to quickly reduce the effective reproduction number well below the 1.0 threshold. Enhancing public health mitigation efforts at the bifurcation tipping points can further accelerate the decline of new cases, followed by enhanced rapid testing and contact tracing when the case numbers are small.

Figure 1.

• Panel (A) Local outbreaks in Beijing, Xinjiang, and Dalian: shock mitigation and rapid testing enabled quick control⁸.
• Panel (B)-(F) COVID-19 epidemic in Ontario, Canada from Feb 26 to Sep 21, 2020. Here, the model by Biao et al.⁹ is used to evaluate the contact rates, diagnosis rate, and quarantine proportions, following the timelines of three stages of social distancing escalation to control the 1st wave, and the three stages of reopening. Social distancing de-escalation led Ontario to reopen on May 11. In our simulation (Fig 1F), with the quarantine proportion increased from 38% to 70% and diagnosis period shortened from 7.5 to 4 days starting on May 16, the reproduction number would have decreased with an accelerated rate of decline, such that the first outbreak would have ended by the end of June and the reopening would have started with very small number of infections. Public health resources could then have been mobilized for swift reaction to any new local hotspot to avoid a full scale second wave. In contrast to this desirable cascading effect — contact reduction leads to a small number of new infections and enables swift public health tracing and testing — Ontario experienced the undesirable cascading effects of relaxation of social distancing too early, such that the exponential growth from a large initial pool of infections quickly led to outbreaks exceeding the tracing and testing capacity.

Author detail: Jianhong Wu^1,3,†, Biao Tang^2,4,†, Yanni Xiao^2,4, Sanyi Tang⁵, Aria Ahmad,⁶ James Orbinski⁶

1 Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada

2 Interdisciplinary Research Center for Mathematics and Life Sciences, Xi’an Jiaotong University, China

3 Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada

4 School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

5 School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, China

6 Dahdaleh Institute for Global Health Research, York University, Toronto, Canada

† The two authors contributed equally

Author contributions: Conceptualization, J.W., B.T. and J.O.; validation and simulation, B.T.; data curation, B.T.; writing—original draft preparation, B.T., Y.X., and S.T.; writing—review and editing, J.W., B.T., and J.O.; All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by the Canadian Institute of Health Research (CIHR) 2019 Novel Coronavirus (COVID-19) rapid research program, the Canada Research Chair Program  and the Natural Sciences and Engineering Research Council of Canada (JW), and the National Natural Science Foundation of China (grant numbers: 11631012 (YX, ST), 61772017, 12031010 (ST)).

Competing interests: The authors declare no competing interests.

References

1. Xiao et al., Linking key intervention timing to rapid decline of the COVID-19 effective reproductive number to quantify lessons from mainland China, Int. J. Infect. Dis. 97, 296-298 (2020).
2. S, Hsiang et al., The effect of large-scale anti-contagion policies on the COVID-19 pandemic, Nature, 584, 262–267 (2020).
3. Tian et al., An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China, Science, 368, 638-642 (2020).
4. J. Kucharski et al., Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study, Lancet Infect. Dis. 20(10), 1151-1160 (2020).
5. WHO Coronavirus Disease (COVID-19) Dashboard, https://covid19.who.int/table. [Accessed at Oct 15, 2020].
6. Sanche et al., High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2. Emerg. Infect. Dis. 26(7), 1470-1477 (2020).
7. Flaxman et al., Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries.
8. National Health Commission of the People’s Republic of China, http://www.nhc.gov.cn/xcs/yqfkdt/gzbd_index.shtml [Accessed on Oct. 26].
9. Tang et al., De-escalation by reversing the escalation with a stronger synergistic package of contact tracing, quarantine, isolation and personal protection: Feasibility of preventing a covid-19 rebound in Ontario, Canada, as a case study. Biology 9(5), 100 (2020).

Paul Clyde, University of Michigan, and Fred Matovu, Chrispus Mayora, and Peter Waiswa, Makerere University

Contact: pclyde@umich.edu

“Part of the challenge here is that we’ve lost the nuance. Some people are saying this is a hoax, it’s fake, it’s not serious. Other people may be saying it’s the worst thing in the world, a zombie apocalypse. It’s neither. This is a terrible pandemic. It has killed 130,000 Americans. It has sickened many, many more. And we don’t yet know what the long-term complications of some of the illness [are]. But it is true that 99% of people who get it will survive.”

— Former CDC Director Dr. Thomas Frieden. [i]

Attempts to curb the pandemic can affect other health and economic targets

Government actions taken in response to the pandemic are often credited or blamed for their effect on the disease itself; less so for the other, more indirect, costs of these actions such as reduced attention to other illnesses.  Part of that may be the lack of concrete numbers.  Decision makers naturally gravitate toward hard numbers, and hard numbers are most readily available for those who contract COVID and those who die with it.  Mortality and morbidity that result from actions taken to try to prevent the spread of the disease are more difficult to measure, much less attribute to specific government policies.

Nonetheless, a number of organizations have started documenting these costs and developing estimates of their magnitude and severity.  For example, with respect to non-COVID health consequences, clinicians are expecting an increase in cancer deaths due to delays in diagnosis and treatment in western markets[ii] or due to constraints occasioned by suspension of public transport in Africa.[iii] For similar reasons, heart disease,[iv] mental health and other chronic diseases can also be expected to increase.[v]

The economic consequences of government actions taken to curb the effects of COVID have been even less clear.  Some point to the significant economic consequences even in Sweden, where the government has employed a relatively light touch, as evidence that the economic downturn is a result of consumer fear rather than government action.  Others assert that, even if there are negative economic consequences from government actions, these surely pale in comparison to saving lives, implying that economic downturns don’t cost lives

Data that speak to the economic consequences of fear are relatively scarce just because we only have a few months to observe.  Existing studies show that the lockdown itself, and not just fear, is having an effect though some suggest the lockdown accounts for a small portion of the economic downturn,[vi] while others suggest lockdowns account for most of the decline.[vii]

Looking at Sweden’s economy highlights another difficulty in isolating the effect of the lockdowns themselves.  Preliminary estimates of Sweden’s GDP in the second quarter suggest that Sweden did not fare well even relative to its neighbors, as it saw GDP drop 8.6% in the second quarter of 2020.  Sweden did, however, fare better than most European countries and than G7 countries except Japan,[viii] the only G7 country that also adopted a relatively light touch.[ix]

The problem with these comparisons is that, like the disease itself, the economic consequences of COVID responses are not confined to political boundaries.  Producers rely on export markets and consumers and producers rely on imports.  Thus, the citizens of Sweden and any other country will suffer the consequences of actions taken by governments all over the world, especially close trading partners.

The link between the economy and mortality rates is clearer, although the health consequences of an economic downturn manifest themselves in other ways as well.  Just as COVID may have long term health effects short of death that are not well understood yet, an economic downturn affects other aspects of health and quality of life.  Nonetheless, since mortality garners substantial attention with respect to COVID, we use research on the relationship between economic downturns and mortality to estimate the potential effect of the economic downturn associated with COVID on mortality alone. In an effort to be concrete and tractable, we focus on the mortality rate of a specific group: children under the age of five in Uganda.

Health and economic impact in sub-Saharan Africa

Since the COVID-19 virus started in Wuhan, China at the end of 2019, it has been both a surprise and a relief to see that the virus itself has killed relatively few people in sub-Sahara Africa.  The economic effect has, however, been devastating.

In Uganda, the Ministry of Finance Planning and Economic Development released April 2020 trade statistics which revealed that exports had fallen by 34.3%. Imports declined by 38.5% over the same period.  This decline is attributed partly to airport closures and travel restrictions more generally.  As but one indication of the severe consequences implied by these figures, fully 42% of tax revenue for Uganda is collected through international trade.[x]

When households have enough income, they are able to make reasonable choices of nutritious foods and other essential goods and services for their household members but, when people do not have enough income, health suffers and mortality rates go up.  Recent data from Uganda shows that outpatient visits, antenatal visits, live births in healthcare facilities, immunizations have all dropped and in many cases, dropped by more than 10% relative to the same period the year before.  The number of children with low birth weight increased relative to the preceding year[xi].  Hence, the reduction in income has led to increased health risks. [xii]

We can quantify the effect of the COVID economic downturn on mortality by using previous estimates of the effect of sudden declines in GDP on mortality.  The GDP growth rate in sub-Saharan Africa is expected to decrease by as much as 8%.  A World Bank study predicted that GDP would decrease from 2.4% in 2019 to as low as -5.1% in 2020.[xiii]  The IMF is expecting a decrease in GDP of 5.4% in 2020.[xiv]  Given the pre-COVID expectations of a GDP increase, this implies a decrease of about 8%.

Using estimates from a study of the effect of the 2008 financial crisis on infant mortality, an 8% decrease in GDP per capita could result in almost 150,000 more infant deaths.[xv]   Estimates from other studies on infants[xvi] and children under 5 are roughly consistent with this, suggesting an 8% decline in GDP could lead to hundreds of thousands more deaths of children under 5.[xvii]

Note that the decline in GDP estimated by the World Bank and IMF was for the overall economy. This will understate the effect on GDP per capita if population is growing, as it is, in sub-Sahara Africa. Thus, the impact of COVID on the economies in many countries in the region is such that hundreds of thousands of children could die.  Indeed, that seems likely.

We need to pay attention to trade-offs

Decision makers in governments around the world are faced with difficult decisions.  Already, more than one million people have contracted COVID and died.  The true number may be higher if some have died with COVID even though the disease was not diagnosed or, instead, may be lower if some people had COVID but died due to some other disease.

The uncertainty surrounding that estimate is much less than the uncertainty about the number we have tried to estimate: the increase in deaths due to the economic consequences of COVID and actions taken to prevent it.  It would be preferable to wait until the data on both COVID mortality and the economic effects were more reliable, but since decisions are being made in real time, we will have to make do with what we have in real time.  It would be a serious mistake to dismiss either the costs or the benefits just because of the uncertainty.

Consider three points regarding the numbers we presented above.

First, we have looked only at the economic effect on the number of deaths of children under five in sub-Sahara Africa.  We do not consider the effect of delayed diagnosis and treatment of other diseases either in Africa or other countries.  We do not discuss the effect on the over-five year child population in sub-Sahara Africa and we do not discuss the effect of poverty in other countries.  All of these populations will also be affected.

Second, government actions to stop COVID will not bring COVID related deaths to zero and are not solely responsible for the decrease in economic activity resulting from the pandemic.  Even in the absence of government actions, there would be negative economic consequences from COVID.  Many are fearful and would choose to stay home and engage in the economy less, even if governments allowed it.  We discussed this earlier but have made no attempt to isolate the effect of government actions and separate them from the “natural” effects on the economy.

However, we have evidence that at least some of the economic consequences are a result of government policy – and some studies suggest policy is the cause of most of the economic downturn.  Similarly, even when governments take relatively drastic actions they do not necessarily reduce mortality dramatically or permanently.  The state of Michigan in the U.S., for instance, has a population that is very similar to Sweden’s, and its governor issued a shelter in place order for 2 months, closing many businesses, limiting the services offered by hospitals, and other limitations.  Yet, despite the stricter measures, the number of deaths related to COVID are about 45% more in Michigan than in Sweden.

Third, the estimates for an increase in mortality rates in sub-Sahara Africa are extremely conservative because they are one-year estimates and many of the newly-impoverished individuals are likely to remain in poverty for many years to come.  Thus, the number of deaths due to the economic downturn will be much higher than the estimates presented above.  To give some idea of the long-term effect, we can return to the 2008 financial crisis.  The World Bank estimated that 70 million people who would not otherwise have been in poverty would remain in poverty into 2020 as a result of the 2008 financial crisis.[xviii]

Looking forward

Globally, about 60 million people die in the world every year.  At the current pace, more than 1.5 million will contract COVID and die.  Oxfam posted a briefing stating that as many as 12,000 hunger deaths per day could be related to COVID.[xix]  Remarkably, their “Actions Needed” did not mention anything about the deleterious effect on mortality of government actions designed to limit the effect of COVID.

But we know anecdotally that the actions governments take to limit COVID deaths have caused people to die.[xx]  We know that hundreds of thousands of children under the age of 5 are likely to die as a result of the economic consequences of COVID.  While fear may be driving some of the economic downturn, government policy is also affecting it and to the extent that it is, governments need to keep these consequences in mind the same way they consider the effect of the disease itself.

During this horrible pandemic our actions, whatever they are, have costs and consequences.  It is therefore important that when developing government policies, we identify and evaluate these as best we can. Let us be done with saying that the tradeoff is between lives saved and the economy as if helping the economy does not also save lives.

Governments have an array of options available to them, and each contains its own set of costs and benefits.  Requiring masks to be worn in public appears to pose few costs, while providing significant benefits by slowing the spread of the virus.  By contrast, forcing businesses to stop offering services does pose a cost, and has economic consequences that extend beyond the borders of the governing authority.  Stopping elective surgeries in areas that are not experiencing significant outbreaks is different from stopping elective surgeries in areas that are facing a shortage of hospital beds due to COVID.  The decisions governments face right now are difficult, but let’s be clear: it is not a question of saving lives or not saving lives.  Rather, it is a question of trying to protect the lives of people from one malady (COVID) as opposed to another (the consequences of serious economic decline and increased poverty).  As Dr Frieden suggests in the quote at the beginning of this article, it requires a more nuanced approach.

References

[i] David Morgan CBS News July 6, 2020, and 12:33 Pm, “U.S. Is an ‘Outlier’ in Global Coronavirus Fight, Former CDC Director Says,” accessed September 21, 2020, https://www.cbsnews.com/news/coronavirus-united-states-former-cdc-director-tom-frieden/.

[ii] Norman E. Sharpless, “COVID-19 and Cancer,” Science 368, no. 6497 (June 19, 2020): 1290–1290, https://doi.org/10.1126/science.abd3377.

[iii] Geoffrey Mutegeki, “Covid 19: Delayed Treatment Puts Cancer Patients At Risk,” accessed September 21, 2020, https://www.newvision.co.ug/news/1517647/covid-19-delayed-treatment-cancer-patients-risk.

[iv] “Fear of COVID-19 Keeping More than Half of Heart Attack Patients Away from Hospitals,” accessed September 21, 2020, https://www.escardio.org/The-ESC/Press-Office/Press-releases/Fear-of-COVID-19-keeping-more-than-half-of-heart-attack-patients-away-from-hospitals, https://www.escardio.org/The-ESC/Press-Office/Press-releases/Fear-of-COVID-19-keeping-more-than-half-of-heart-attack-patients-away-from-hospitals.

[v] Steven H. Woolf et al., “Excess Deaths From COVID-19 and Other Causes, March-April 2020,” JAMA 324, no. 5 (August 4, 2020): 510–13, https://doi.org/10.1001/jama.2020.11787.

[vi] Austan Goolsbee and Chad Syverson, “Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline 2020,” Working Paper, Working Paper Series (National Bureau of Economic Research, June 2020), https://doi.org/10.3386/w27432.

[vii] Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber, “The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending,” Working Paper, Working Paper Series (National Bureau of Economic Research, May 2020), https://doi.org/10.3386/w27141.

[viii] OECD, “GDP and Spending – Quarterly GDP – OECD Data,” OECD, accessed September 21, 2020, http://data.oecd.org/gdp/quarterly-gdp.htm.

[ix] Eric Feldman, “Did Japan’s Lenient Lockdown Conquer the Coronavirus? | The Regulatory Review,” June 10, 2020, https://www.theregreview.org/2020/06/10/feldman-japan-lenient-lockdown-conquer-coronavirus/.

[x] Ali Twaha, “Covid 19: Uganda’s Exports Decline By 34.3%,” accessed September 21, 2020, https://www.newvision.co.ug/news/1521359/covid-19-uganda-exports-decline-34.

[xi] Ministry of Health, Uganda, “Impact of COVID-19 Containment Measures on Reproductive Maternal Newborn Child Health and Adolescent Health and HIV Service Delivery and Utilization in Busoga Sub Region, Uganda,” August 2020.

[xii] Jed Friedman and Norbert Schady, “How Many Infants Likely Died in Africa as a Result of the 2008–2009 Global Financial Crisis?,” Health Economics 22, no. 5 (2013): 611–22, https://doi.org/10.1002/hec.2818.

[xiii] The World Bank, “Overview,” Text/HTML, World Bank, accessed September 21, 2020, https://www.worldbank.org/en/region/afr/overview.

[xiv] IMF, “Six Charts That Show Sub-Saharan Africa’s Sharpest Economic Contraction Since the 1970s,” IMF, accessed September 21, 2020, https://www.imf.org/en/News/Articles/2020/06/27/na062720-six-charts-show-how-the-economic-outlook-has-deteriorated-in-sub-saharan-africa-since-april.

[xv] Friedman and Schady, “How Many Infants Likely Died in Africa as a Result of the 2008–2009 Global Financial Crisis?”

[xvi] Sarah Baird, Jed Friedman, and Norbert Schady, “Aggregate Income Shocks and Infant Mortality in the Developing World,” The Review of Economics and Statistics 93, no. 3 (August 5, 2010): 847–56, https://doi.org/10.1162/REST_a_00084; Friedman and Schady, “How Many Infants Likely Died in Africa as a Result of the 2008–2009 Global Financial Crisis?”

[xvii] Stephan Klasen, “Poverty, Undernutrition, and Child Mortality: Some Inter-Regional Puzzles and Their Implications for Research and Policy,” The Journal of Economic Inequality 6, no. 1 (March 1, 2008): 89–115, https://doi.org/10.1007/s10888-007-9056-x.

[xviii] The World Bank, The World Bank Group’s Response to the Global Economic Crisis, Independent Evaluation Group Studies (The World Bank, 2011), https://doi.org/10.1596/978-0-8213-8665-1.

[xix] Oxfam, “The Hunger Virus: How COVID-19 Is Fueling Hunger In A Hungry World,” accessed September 21, 2020, /explore/research-publications/hunger-virus-how-covid-19-fueling-hunger-hungry-world/.

[xx] Evelyn Lirri, “How Uganda’s Tough Approach to Covid-19 Is Hurting Its Citizens,” accessed September 21, 2020, https://www.telegraph.co.uk/global-health/science-and-disease/ugandas-tough-approach-covid-19-hurting-citizens/.

Vikrum Malhotra MD, Avni Thakore MD, Monika Minhas, Manpreet Kaur

Contact: vikrum.malhotra@gmail.com

The COVID-19 pandemic has forced the healthcare industry to confront extraordinary challenges. Healthcare providers have endured shortages in medical supplies, protective equipment, and market instability. Nonetheless, amid the crisis, strong public health measures, physical distancing, and transitioning to online services has provided reassurance that the COVID-19 curve may be flattening as the number of new cases has decreased and the number of patients that are hospitalized has stabilized.

These challenges and changes have ushered in a new era for the healthcare industry, including the transition to hybrid in-person and digital health practice models. As we continue to move toward the transition of healthcare from pre- to post-COVID-19, certain provisions can ensure the safety, efficiency, and effectiveness in healthcare delivery models. This article describes key changes in operations, communication, and security that are central to moving ahead effectively, including designation of specialty care facilities, creating emergency management plans, changing appointment policies, coordinating social distancing, managing hygiene protocols, and expanding testing.

Operations, Communication, and Security 

Designation of Specialty Care Facilities

Governments should participate in a coordinated and effective method of hospital utilization by considering the designation of select hospitals as COVID-19-only specialty care facilities. These facilities will ensure properly trained staff, adequate PPE, expertise in treating complex patients, and proper ICU capacity (including negative pressure rooms when needed) for patients. This allows other hospitals to continue ongoing services to non-COVID-19 patients with active medical conditions without risking transmission and contamination. Governments should also utilize their resources to plan for the management of outbreaks in vulnerable populations and locations.

Emergency Management Plans and Teams

Healthcare practices should maintain emergency management plans that outline practice operations and the impacts on healthcare providers and patients that coronavirus may have.  These plans can include pandemic coordinators, risk communications, essential functions, delegations of authority, continuity of facilities and communications, human resources, essential records management, testing and training, and reconstitution planning. They may choose to implement shift work for multispecialty office practices with providers staggered over the course of a day to reduce waiting room crowding and infection risk.

Consideration should also be given to assembling physician-staff teams so that monitoring the spread of coronavirus is attainable and practices may continue to operate in the event of positive results within a team. Clinic staffing must be structured to limit exposure and mitigate risk in the event a staff member gets sick as contact tracing and active testing become the new norm.

Practices should have continued access to communication that can be established by testing remote access to servers and email systems or the use of secure and cost-effective cloud options if access is restricted. Information should be kept within HIPAA regulations to ensure the security of sensitive data. Practices should also have access to critical information such as updates from CDC on COVID-19 for healthcare professionals.

Appointment Policies

Providers should institute a protocol for telemedicine to reduce the risk of transmission. Telemedicine appointments can include chronic care management, primary care, mental health, post-hospital discharge follow-up and post-op follow-up, thereby allowing providers and patients to maintain the practice of physical distancing while still providing timely, efficient and effective continuity of care. Patients can also utilize online payment methods via patient portals for billing and co-pays if needed. Providers should emphasize that non-urgent care can be obtained through telemedicine or rescheduled for a later time following local and state guidelines.

Nonetheless, offices should remain open for emergent needs or those requiring management in-office. Appropriate spacing of in-person appointments must be made to reduce wait times in the waiting room and allow for efficient evaluation of patients.  On arrival, patients should have options to self check-in via a tablet service to reduce crowding. Patients needing to visit a practice can also be checked-in by phone and advised to wait in the parking area until called in. Patients can also be screened via questionnaires and administered temperature checks before entering the practice to further reduce the risk of transmission.

Social Distancing

Continuing social distancing practices post-COVID is crucial to the operations of healthcare practices. Providers should only allow patients inside the practice, restricting family members, friends, and children from entering except in extenuating circumstances. If there is any reason that this restriction cannot be met, only one person should accompany the patient. Practices should place signs that discourage sitting in waiting areas and tape off distances of six feet per CDC guidelines.

Hygiene Protocols

Developing protocols for proper hand hygiene, PPE, and sanitation of equipment should be a priority for healthcare practices. Providers may require the use of hand sanitizer by all patients and visitors to the practice and increase postage of signs in restrooms for proper hand hygiene. To further protect staff and patients, the installation of “sneeze guards” should be implemented on all diagnostic equipment that requires close contact and proper protective wear should be made available to all physicians and staff. Practices should also initiate frequent, thorough disinfection of waiting areas, restrooms, exam rooms, administrative tools, and equipment.

Expanding COVID-19 Testing

As testing capabilities expand, appropriate use of diagnostic testing for COVID-19 — ideally at the point of care — should be incorporated into pre-admission testing requirements prior to surgery and invasive procedures. Timely screening of healthcare personnel with known exposure risk should also be implemented in human resources guidelines. As more knowledge is gleaned regarding antibody testing for potential immunity to COVID-19, this testing modality should be incorporated to position personnel at lower risk of infection appropriately in critical care environments.

Looking Forward

Government and healthcare collaborations will prove to be critical tools in the functioning of healthcare practices. Protocols geared toward operations, appointments, social distancing, and hygiene and workflows that incorporate advances in COVID-19 testing can help practices transition toward a “new normal” in the post-COVID era. These guidelines can help ensure the safety of patients, physicians, and healthcare personnel and allow for effective and efficient healthcare.

Stephen Lewis, MD, MHCDS, and Bryan Spear, MBA, MHA

Contact: Stephen.lewis1972@gmail.com

Predictable Surprises

Max Bazerman and Michael Watkins [1] defined the term predictable surprise as the disasters that organizations should see coming.  They note organizations routinely fail to take one or more of three key steps in avoiding predictable surprises:  recognition, prioritization, and mobilization.

The COVID-19 novel corona virus – and especially the follow up to it — is a predictable surprise. The pandemic abruptly changed healthcare operations as health system leaders prepared for possible hotspots in COVID-19 demand surges. These operational reprioritizations, new social distancing requirements, uncertainties, and significant fear surrounding the virus led to abrupt stoppage in the delivery of a significant proportion of routine healthcare. The pace of change in healthcare delivery also required relatively decentralized decision making, with clinics and services individually determining cancellation, rescheduling, and staffing policies, as well as telehealth practices.

While the pressure from a growing queue of patients eased with the expansion of telehealth, not all patients’ needs can be addressed virtually. Many patients who have specific needs regarding diagnostic testing or chronic condition management face increased acuity. This presents a unique, but predictable, problem when considering planning for the return to “new normal” healthcare delivery operations.

A Framework to View the Problem

As organizations consider approaches to re-expand direct patient care inside of a new and evolving paradigm for healthcare delivery, key externalities can be easily recognized. Based on the accumulation of patients with delayed care, providers can expect the acuity level of some patients to rise due to a lack of treatment. The figure below depicts a phenomenon in which, while the hospital begins to reopen fully, the system will be stressed beyond its available resources even after the COVID specific patient surge passes.

There are two main elements to understanding this problem.

• Capacity: First is the capacity of the healthcare system post COVID surge. As local restrictions ease, staff will seek and deserve time off to personally recover from the COVID surge which will lower the overall capacity of some services.
• Demand challenges: Second, there will be a rapid demand increase, above the rolling 12-month average, of interdependent services such as referrals, diagnostics, and administrative support. This is attributable to patients with above average acuity re-entering the system first.  These patients’ complexity, exacerbated by breaks in continuity of care, may drive above-average requirements for diagnostic testing, referrals, and care management.

These two elements — post-surge decreased staffing capacity plus increased demand — create a predictable surprise for the many hospitals seeking a path forward towards resuming normal operations.

Gathering Data for Solutions

To address this utilization risk, healthcare systems should start at the clinic and service level to understand how they delivered care during the first several weeks of the COVID-19 crisis.  It is important to prioritize understanding patient risk stratification approaches, medical service interdependencies, and how virtual health was implemented.

We recognize that only specialists can appropriately risk-stratify their patients. Discussions with these providers suggested global population risk assessment and prioritization was less familiar to specialists. Our observations also indicated significant heterogeneity across the organization in how virtual health was operationalized.

Solving the problem at each institution requires two types of information from key stakeholders within an organization.

• Delivery gaps: First, to capture variation and associated healthcare delivery gaps, senior leaders can send each front-line leader a comprehensive survey with three main sections: Access to Care, Continuity of Care, and Virtual Health.
• Reopening: Second, senior leaders should also present front-line leaders with a series of questions to prepare for reopening, again with three main topics: Staff Reintegration and Capacity Planning; Clinic Operations; and Physical Space Preparation.

The goal is to pair this information with centrally gathered utilization data to tailor operational recovery plans to specific clinic and service needs while creating an integrated global plan.

Looking Forward

Reflecting on our core mission to provide safe, timely, and effective care, we are all preparing to prevent a predictable utilization surprise associated with rapid and variable healthcare delivery operational changes. We should seek a comprehensive coordinated solution built upon customized local solutions that address specific clinical and service differences.

References

[1] Bazerman, M. H., & Watkins, M. (2004). Predictable surprises : the disasters you should have seen coming, and how to prevent them. Boston: Harvard Business School Press.