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The U.S. could see a second wave soon. Hospitals already on the brink fear 'disaster.'

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The U.S. could see a second wave soon. Hospitals already on the brink fear 'disaster.'

Posted | Updated by Insights team:

Publication | Update:

Sep 2020
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When Dr. Shereef Elnahal walked through his New Jersey hospital in April, he couldn’t believe what he was seeing.

There were 300 patients being treated for Covid-19, filling hospital rooms and spilling out into the halls of the emergency room. The trauma center, once used for gunshot wounds and car crash victims, was now filled with people on ventilators.

“It was really like nothing we’ve ever seen before,” said Elnahal, president and CEO of University Hospital in Newark.

“I have memories of walking around and I would look inside the rooms where that was possible. Almost every person was a person of color,” he told NBC News.

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Elnahal’s hospital is one of the more than 100 major medical centers that treat America’s most vulnerable patients: communities of color who have been disproportionately harmed by Covid-19. Data has increasingly shown that Black and Hispanic patients are more likely to be hospitalized with the virus and, in many cases, more likely to die from it.

“We’re learning more and more that it’s these vulnerable communities being hit harder by the pandemic,” said Beth Feldpush, senior vice president of policy and advocacy for America’s Essential Hospitals, a group representing the more than 300 hospitals that treat uninsured patients. “Our hospitals are absolutely serving those hardest-hit communities.”

A second surge of Covid-19 this fall and winter could be catastrophic for the U.S., and it’s not just more sick people that doctors worry about. The very hospitals that treat lower-income patients could be forced to shut down or cut crucial services.

“We would absolutely be at risk of closing,” Elnahal said. “It would be a public health disaster for this community.”

The pandemic hit all U.S. hospitals with a financial “triple whammy,” said Aaron Wesolowski, the American Hospital Association’s vice president for policy research, analytics and strategy. Costs increased dramatically, while revenues plummeted.

The hospitals were forced to cover the exorbitant costs of buying extra personal protective equipment like N95 masks, as well as convert wards to treat Covid-19 patients and more uninsured patients. At the same time, they had to stop performing revenue-generating procedures like elective surgeries.

By the end of 2020, hospitals across the U.S. will lose about $ 300 billion, according to the American Hospital Association. But for major medical centers like University Hospital in Newark, the financial hit of a second wave of Covid-19 would be especially devastating.

“Where there are already cracks in the system, those cracks become earthquakes,” said Dr. Chris Pernell, University Hospital’s chief of strategic integration and health equity officer.

That’s because these safety net hospitals are nonprofit and promise care for all patients, regardless of insurance coverage. Even before the pandemic, they operated on shaky budgets. Jackson Health System in Miami, for example, only has enough cash on hand to operate for 50 days. Private hospitals typically have more than triple that amount of cash in reserve.

'Where else would they go?'

Mark Knight, chief financial officer for Jackson Health System, said if it wasn’t for an injection of federal funds, the hospital system, which serves between 1,200 and 1,300 patients a day, could have been in a dire situation.

“This year would have been a fiscal disaster,” he said, adding that the local government serves as a backstop in case they reach a crisis point.

While most safety net hospitals, including Jackson Health and University Hospital, received federal funds this year to bail them out, others like Valleywise Health in Phoenix got nothing. Chief medical officer Dr. Michael White blamed a glitch in the formula used to calculate which safety net hospitals would receive money through the CARES Act Provider Relief Fund.

The health system, which serves more than 400,000 mostly Hispanic patients annually, is still trying to figure out the extent of the loss from the first Covid-19 wave. If a second one hits, White said, they may be forced to cut services.

“Any time you see health care services decrease, there’s diminished access to care for those that are the most vulnerable and who need it most,” White said.

A recent study from the University of California, Berkeley, revealed that there may be racial bias in the formula the government used to distributeCARES funds to hospitals, leaving some predominantly Black and Hispanic communities shortchanged, even though they were hit harder by the pandemic.

“Looking at how the funding went out the door, providers serving those vulnerable communities did not get as much of the funding,” Feldpush said, adding that later distributions did target certain Covid-19 hotspot hospitals.

The hope is that if there is a resurgence, the federal government will step in again, but there is no guarantee. In fact, Feldpush said the Department of Health and Human Services still has another $ 50 billion allocated for providers that should have already been distributed. Even that won’t be enough to cover the costs of a resurgence of Covid-19.

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In the meantime, hospitals are preparing for the second wave, stocking up on PPE despite lingering shortages and implementing lessons learned from the first surges, especially when it comes to treatment.

However, for the patients who rely on these hospitals, their concerns aren’t about budgets and bottom lines.

“The hospital means everything to the community,” said 49-year-old Tawanda Sheard, a University Hospital employee who has lived in the neighborhood for 25 years and was hospitalized there with Covid-19 last spring. “They might not have the best insurance, but they know they can come there. Where else would they go to get treated?”

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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