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3.
Am J Public Health ; 111(12): 2157-2166, 2021 12.
Article in English | MEDLINE | ID: covidwho-1559064

ABSTRACT

The COVID-19 pandemic caused substantial disruptions in the field operations of all 3 major components of the Medical Expenditure Panel Survey (MEPS). The MEPS is widely used to study how policy changes and major shocks, such as the COVID-19 pandemic, affect insurance coverage, access, and preventive and other health care utilization and how these relate to population health. We describe how the MEPS program successfully responded to these challenges by reengineering field operations, including survey modes, to complete data collection and maintain data release schedules. The impact of the pandemic on response rates varied considerably across the MEPS. Investigations to date show little effect on the quality of data collected. However, lower response rates may reduce the statistical precision of some estimates. We also describe several enhancements made to the MEPS that will allow researchers to better understand the impact of the pandemic on US residents, employers, and the US health care system. (Am J Public Health. 2021;111(12):2157-2166. https://doi.org/10.2105/AJPH.2021.306534).


Subject(s)
COVID-19/epidemiology , Health Expenditures/statistics & numerical data , Surveys and Questionnaires/statistics & numerical data , Electronic Health Records/statistics & numerical data , Health Services/statistics & numerical data , Humans , Insurance Coverage/organization & administration , Insurance Coverage/statistics & numerical data , Pandemics , Patient Acceptance of Health Care/statistics & numerical data , Population Health/statistics & numerical data , Quality of Health Care/statistics & numerical data , SARS-CoV-2 , Telemedicine/statistics & numerical data , United States/epidemiology
8.
JAMA Netw Open ; 4(10): e2129894, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1473780

ABSTRACT

Importance: Many insurers waived cost sharing for COVID-19 hospitalizations during 2020. Nonetheless, patients may have been billed if their plans did not implement waivers or if waivers did not capture all hospitalization-related care. Assessment of out-of-pocket spending for COVID-19 hospitalizations in 2020 may show the financial burden that patients may experience if insurers allow waivers to expire, as many chose to do during 2021. Objective: To estimate out-of-pocket spending for COVID-19 hospitalizations in the US in 2020. Design, Setting, and Participants: This cross-sectional study used data from the IQVIA PharMetrics Plus for Academics Database, a national claims database representing 7.7 million privately insured patients and 1.0 million Medicare Advantage patients, regarding COVID-19 hospitalizations for privately insured and Medicare Advantage patients from March to September 2020. Main Outcomes and Measures: Mean total out-of-pocket spending, defined as the sum of out-of-pocket spending for facility services billed by hospitals (eg, accommodation charges) and professional and ancillary services billed by clinicians and ancillary providers (eg, clinician inpatient evaluation and management, ambulance transport). Results: Analyses included 4075 hospitalizations; 2091 (51.3%) were for male patients, and the mean (SD) age of patients was 66.8 (14.8) years. Of these hospitalizations, 1377 (33.8%) were for privately insured patients. Out-of-pocket spending for facility services, professional and ancillary services, or both was reported for 981 of 1377 hospitalizations for privately insured patients (71.2%) and 1324 of 2968 hospitalizations for Medicare Advantage patients (49.1%). Among these hospitalizations, mean (SD) total out-of-pocket spending was $788 ($1411) for privately insured patients and $277 ($363) for Medicare Advantage patients. In contrast, out-of-pocket spending for facility services was reported for 63 hospitalizations for privately insured patients (4.6%) and 36 hospitalizations for Medicare Advantage patients (1.3%). Among these hospitalizations, mean (SD) total out-of-pocket spending was $3840 ($3186) for privately insured patients and $1536 ($1402) for Medicare Advantage patients. Total out-of-pocket spending exceeded $4000 for 2.5% of privately insured hospitalizations compared with 0.2% of Medicare Advantage hospitalizations. Conclusions and Relevance: In this cross-sectional study, few patients hospitalized for COVID-19 in 2020 were billed for facility services provided by hospitals, suggesting that most were covered by insurers with cost-sharing waivers. However, many patients were billed for professional and ancillary services, suggesting that insurer cost-sharing waivers may not have covered all hospitalization-related care. High cost sharing for patients who were billed by facility services suggests that out-of-pocket spending may be substantial for patients whose insurers have allowed waivers to expire.


Subject(s)
COVID-19/epidemiology , Health Expenditures/statistics & numerical data , Hospitalization/economics , Aged , Aged, 80 and over , COVID-19/economics , Cost Sharing/statistics & numerical data , Cross-Sectional Studies , Databases, Factual , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2
10.
Lancet ; 398(10308): 1317-1343, 2021 10 09.
Article in English | MEDLINE | ID: covidwho-1433921

ABSTRACT

BACKGROUND: The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. METHODS: We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. FINDINGS: In 2019, health spending globally reached $8·8 trillion (95% uncertainty interval [UI] 8·7-8·8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40·4 billion (0·5%, 95% UI 0·5-0·5) was development assistance for health provided to low-income and middle-income countries, which made up 24·6% (UI 24·0-25·1) of total spending in low-income countries. We estimate that $54·8 billion in development assistance for health was disbursed in 2020. Of this, $13·7 billion was targeted toward the COVID-19 health response. $12·3 billion was newly committed and $1·4 billion was repurposed from existing health projects. $3·1 billion (22·4%) of the funds focused on country-level coordination and $2·4 billion (17·9%) was for supply chain and logistics. Only $714·4 million (7·7%) of COVID-19 development assistance for health went to Latin America, despite this region reporting 34·3% of total recorded COVID-19 deaths in low-income or middle-income countries in 2020. Spending on health is expected to rise to $1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. INTERPRETATION: Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
COVID-19/prevention & control , Developing Countries/economics , Economic Development , Healthcare Financing , International Agencies/economics , COVID-19/economics , COVID-19/epidemiology , Financing, Government/economics , Financing, Government/organization & administration , Global Health/economics , Government Programs/economics , Government Programs/organization & administration , Government Programs/statistics & numerical data , Government Programs/trends , Gross Domestic Product , Health Expenditures/statistics & numerical data , Health Expenditures/trends , Humans , International Agencies/organization & administration , International Cooperation
11.
Health Serv Res ; 57(1): 15-26, 2022 02.
Article in English | MEDLINE | ID: covidwho-1405159

ABSTRACT

OBJECTIVE: To estimate the impact of the $600 per week Federal Pandemic Unemployment Compensation (FPUC) payments on health care services spending during the Covid pandemic and to investigate if this impact varied by state Medicaid expansion status. DATA SOURCES: This study leverages novel, publicly available data from Opportunity Insights capturing consumer credit and debit card spending on health care services for January 18-August 15, 2020 as well as information on unemployment insurance claims, Covid cases, and state policy changes. STUDY DESIGN: Using triple-differences estimation, we leverage two sources of variation-within-state change in the unemployment insurance claims rate and the introduction of FPUC payments-to estimate the moderating effect of FPUC on health care spending losses as unemployment rises. Results are stratified by state Medicaid expansion status. EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: For each percentage point increase in the unemployment insurance claims rate, health care spending declined by 1.0% (<0.05) in Medicaid expansion states and by 2.0% (<0.01) in nonexpansion states. However, FPUC partially mitigated this association, boosting spending by 0.8% (<0.001) and 1.3% (<0.05) in Medicaid expansion and nonexpansion states, respectively, for every percentage point increase in the unemployment insurance claims rate. CONCLUSIONS: We find that FPUC bolstered health care spending during the Covid pandemic, but that both the negative consequences of unemployment and moderating effects of federal income supports were greatest in states that did not adopt Medicaid expansion. These results indicate that emergency federal spending helped to sustain health care spending during a period of rising unemployment. Yet, the effectiveness of this program also suggests possible unmet demand for health care services, particularly in states that did not adopt Medicaid expansion.


Subject(s)
COVID-19/economics , Health Expenditures/statistics & numerical data , Health Services Accessibility/economics , Medicaid/economics , Unemployment/statistics & numerical data , COVID-19/epidemiology , Humans , Patient Protection and Affordable Care Act , United States
12.
JAMA ; 326(7): 649-659, 2021 08 17.
Article in English | MEDLINE | ID: covidwho-1359741

ABSTRACT

Importance: Measuring health care spending by race and ethnicity is important for understanding patterns in utilization and treatment. Objective: To estimate, identify, and account for differences in health care spending by race and ethnicity from 2002 through 2016 in the US. Design, Setting, and Participants: This exploratory study included data from 7.3 million health system visits, admissions, or prescriptions captured in the Medical Expenditure Panel Survey (2002-2016) and the Medicare Current Beneficiary Survey (2002-2012), which were combined with the insured population and notified case estimates from the National Health Interview Survey (2002; 2016) and health care spending estimates from the Disease Expenditure project (1996-2016). Exposure: Six mutually exclusive self-reported race and ethnicity groups. Main Outcomes and Measures: Total and age-standardized health care spending per person by race and ethnicity for each year from 2002 through 2016 by type of care. Health care spending per notified case by race and ethnicity for key diseases in 2016. Differences in health care spending across race and ethnicity groups were decomposed into differences in utilization rate vs differences in price and intensity of care. Results: In 2016, an estimated $2.4 trillion (95% uncertainty interval [UI], $2.4 trillion-$2.4 trillion) was spent on health care across the 6 types of care included in this study. The estimated age-standardized total health care spending per person in 2016 was $7649 (95% UI, $6129-$8814) for American Indian and Alaska Native (non-Hispanic) individuals; $4692 (95% UI, $4068-$5202) for Asian, Native Hawaiian, and Pacific Islander (non-Hispanic) individuals; $7361 (95% UI, $6917-$7797) for Black (non-Hispanic) individuals; $6025 (95% UI, $5703-$6373) for Hispanic individuals; $9276 (95% UI, $8066-$10 601) for individuals categorized as multiple races (non-Hispanic); and $8141 (95% UI, $8038-$8258) for White (non-Hispanic) individuals, who accounted for an estimated 72% (95% UI, 71%-73%) of health care spending. After adjusting for population size and age, White individuals received an estimated 15% (95% UI, 13%-17%; P < .001) more spending on ambulatory care than the all-population mean. Black (non-Hispanic) individuals received an estimated 26% (95% UI, 19%-32%; P < .001) less spending than the all-population mean on ambulatory care but received 19% (95% UI, 3%-32%; P = .02) more on inpatient and 12% (95% UI, 4%-24%; P = .04) more on emergency department care. Hispanic individuals received an estimated 33% (95% UI, 26%-37%; P < .001) less spending per person on ambulatory care than the all-population mean. Asian, Native Hawaiian, and Pacific Islander (non-Hispanic) individuals received less spending than the all-population mean on all types of care except dental (all P < .001), while American Indian and Alaska Native (non-Hispanic) individuals had more spending on emergency department care than the all-population mean (estimated 90% more; 95% UI, 11%-165%; P = .04), and multiple-race (non-Hispanic) individuals had more spending on emergency department care than the all-population mean (estimated 40% more; 95% UI, 19%-63%; P = .006). All 18 of the statistically significant race and ethnicity spending differences by type of care corresponded with differences in utilization. These differences persisted when controlling for underlying disease burden. Conclusions and Relevance: In the US from 2002 through 2016, health care spending varied by race and ethnicity across different types of care even after adjusting for age and health conditions. Further research is needed to determine current health care spending by race and ethnicity, including spending related to the COVID-19 pandemic.


Subject(s)
/statistics & numerical data , Health Expenditures/statistics & numerical data , Healthcare Disparities/ethnology , /statistics & numerical data , Health Care Surveys , Humans , United States
13.
JAMA ; 326(3): 250-256, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1338163

ABSTRACT

Importance: Medical debt is an increasing concern in the US, yet there is limited understanding of the amount and distribution of medical debt, and its association with health care policies. Objective: To measure the amount of medical debt nationally and by geographic region and income group and its association with Medicaid expansion under the Affordable Care Act. Design, Setting, and Participants: Data on medical debt in collections were obtained from a nationally representative 10% panel of consumer credit reports between January 2009 and June 2020 (reflecting care provided prior to the COVID-19 pandemic). Income data were obtained from the 2014-2018 American Community Survey. The sample consisted of 4.1 billion person-month observations (nearly 40 million unique individuals). These data were used to estimate the amount of medical debt (nationally and by geographic region and zip code income decile) and to examine the association between Medicaid expansion and medical debt (overall and by income group). Exposures: Geographic region (US Census region), income group (zip code income decile), and state Medicaid expansion status. Main Outcomes and Measures: The stock (all unpaid debt listed on credit reports) and flow (new debt listed on credit reports during the preceding 12 months) of medical debt in collections that can be collected on by debt collectors. Results: In June 2020, an estimated 17.8% of individuals had medical debt (13.0% accrued debt during the prior year), and the mean amount was $429 ($311 accrued during the prior year). The mean stock of medical debt was highest in the South and lowest in the Northeast ($616 vs $167; difference, $448 [95% CI, $435-$462]) and higher in poor than in rich zip code income deciles ($677 vs $126; difference, $551 [95% CI, $520-$581]). Between 2013 and 2020, the states that expanded Medicaid in 2014 experienced a decline in the mean flow of medical debt that was 34.0 percentage points (95% CI, 18.5-49.4 percentage points) greater (from $330 to $175) than the states that did not expand Medicaid (from $613 to $550). In the expansion states, the gap in the mean flow of medical debt between the lowest and highest zip code income deciles decreased by $145 (95% CI, $95-$194) while the gap increased by $218 (95% CI, $163-$273) in the nonexpansion states. Conclusions and Relevance: This study provides an estimate of the amount of medical debt in collections in the US based on consumer credit reports from January 2009 to June 2020, reflecting care delivered prior to the COVID-19 pandemic, and suggests that the amount of medical debt was highest among individuals living in the South and in lower-income communities. However, further study is needed regarding debt related to COVID-19.


Subject(s)
Financing, Personal/economics , Health Expenditures/statistics & numerical data , Healthcare Disparities/economics , Humans , Income , Insurance, Health/economics , Medicaid/economics , Medically Uninsured , Social Determinants of Health , United States
14.
Am J Public Health ; 110(S2): S194-S196, 2020 07.
Article in English | MEDLINE | ID: covidwho-1242253

ABSTRACT

Objectives. To examine the accuracy of official estimates of governmental health spending in the United States.Methods. We coded approximately 2.7 million administrative spending records from 2000 to 2018 for public health activities according to a standardized Uniform Chart of Accounts produced by the Public Health Activities and Services Tracking project. The official US Public Health Activity estimate was recalculated using updated estimates from the data coding.Results. Although official estimates place governmental public health spending at more than $93 billion (2.5% of total spending on health), detailed examination of spending records from state governments shows that official estimates include substantial spending on individual health care services (e.g., behavioral health) and that actual spending on population-level public health activities is more likely between $35 billion and $64 billion (approximately 1.5% of total health spending).Conclusions. Clarity in understanding of public health spending is critical for characterizing its value proposition. Official estimates are likely tens of billions of dollars greater than actual spending.Public Health Implications. Precise and clear spending estimates are material for policymakers to accurately understand the effect of their resource allocation decisions.


Subject(s)
Public Health/economics , State Government , Health Expenditures/statistics & numerical data , Humans , United States
15.
Lancet ; 397(10288): 2012-2022, 2021 05 22.
Article in English | MEDLINE | ID: covidwho-1219074

ABSTRACT

The health and care sector plays a valuable role in improving population health and societal wellbeing, protecting people from the financial consequences of illness, reducing health and income inequalities, and supporting economic growth. However, there is much debate regarding the appropriate level of funding for health and care in the UK. In this Health Policy paper, we look at the economic impact of the COVID-19 pandemic and historical spending in the UK and comparable countries, assess the role of private spending, and review spending projections to estimate future needs. Public spending on health has increased by 3·7% a year on average since the National Health Service (NHS) was founded in 1948 and, since then, has continued to assume a larger share of both the economy and government expenditure. In the decade before the ongoing pandemic started, the rate of growth of government spending for the health and care sector slowed. We argue that without average growth in public spending on health of at least 4% per year in real terms, there is a real risk of degradation of the NHS, reductions in coverage of benefits, increased inequalities, and increased reliance on private financing. A similar, if not higher, level of growth in public spending on social care is needed to provide high standards of care and decent terms and conditions for social care staff, alongside an immediate uplift in public spending to implement long-overdue reforms recommended by the Dilnot Commission to improve financial protection. COVID-19 has highlighted major issues in the capacity and resilience of the health and care system. We recommend an independent review to examine the precise amount of additional funds that are required to better equip the UK to withstand further acute shocks and major threats to health.


Subject(s)
COVID-19/economics , Health Expenditures/statistics & numerical data , Health Policy/economics , State Medicine/economics , Financing, Government , Humans , Social Support , United Kingdom
16.
Health Syst Reform ; 7(1): e1897323, 2021 01 01.
Article in English | MEDLINE | ID: covidwho-1207212

ABSTRACT

As countries all over the world grapple with containing the COVID-19 outbreak, Low- and Middle-Income Countries (LMICs) are particularly hard-pressed because on the one hand, the pandemic has created unforeseen high demand for health services which requires increased spending. On the other hand, the contagion and the public health measures taken to curb it have disrupted economies whilst creating additional spending pressures as well. This constrains the policy options available for LMICs to ensure an adequate and sustainable financing for the health sector's COVID-19 response whilst maintaining routine supply of essential health services. Despite this, as demonstrated by India, many LMICs are undertaking many reform efforts to address both the health and economic hardships caused by the pandemic. In this commentary, we describe the policy tools that one such LMIC, India, has used to enable financing for the outbreak.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Family Characteristics , Health Expenditures/statistics & numerical data , Developing Countries , Female , Humans , India/epidemiology , Male , Pandemics , SARS-CoV-2
18.
J Manag Care Spec Pharm ; 26(11): 1468-1474, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-1200401

ABSTRACT

The COVID-19 pandemic and the social unrest pervading U.S. cities in response to the killings of George Floyd and other Black citizens at the hands of police are historically significant. These events exemplify dismaying truths about race and equality in the United States. Racial health disparities are an inexcusable lesion on the U.S. health care system. Many health disparities involve medications, including antidepressants, anticoagulants, diabetes medications, drugs for dementia, and statins, to name a few. Managed care pharmacy has a role in perpetuating racial disparities in medication use. For example, pharmacy benefit designs are increasingly shifting costs of expensive medications to patients, creating affordability crises for lower income workers, who are disproportionally persons of color. In addition, the quest to maximize rebates serves to inflate list prices paid by the uninsured, among which Black and Hispanic people are overrepresented. While medication cost is a foremost barrier for many patients, other factors also propagate racial disparities in medication use. Even when cost sharing is minimal or zero, medication adherence rates have been documented to be lower among Blacks as compared with Whites. Deeper understandings are needed about how racial disparities in medication use are influenced by factors such as culture, provider bias, and patient trust in medical advice. Managed care pharmacy can address racial disparities in medication use in several ways. First, it should be acknowledged that racial disparities in medication use are pervasive and must be resolved urgently. We must not believe that entrenched health system, societal, and political structures are impermeable to change. Second, the voices of community members and their advocates must be amplified. Coverage policies, program designs, and quality initiatives should be developed in consultation with those directly affected by racial disparities. Third, the industry should commit to dramatically reducing patient cost sharing for essential medication therapies. Federal and state efforts to limit annual out-of-pocket pharmacy spending should be supported, even though increased premiums may be an undesirable (yet more equitable) consequence. Finally, information about race should be incorporated into all internal and external reporting and quality improvement activities. DISCLOSURES: No funding was received for the development of this manuscript. Kogut is partially supported by Institutional Development Award Numbers U54GM115677 and P20GM125507 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds Advance Clinical and Translational Research (Advance-CTR), and the RI Lifespan Center of Biomedical Research Excellence (COBRE) on Opioids and Overdose, respectively. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health.


Subject(s)
Coronavirus Infections/epidemiology , Health Status Disparities , Managed Care Programs/organization & administration , Pharmaceutical Services/organization & administration , Pneumonia, Viral/epidemiology , /statistics & numerical data , African Americans , Betacoronavirus , COVID-19 , Cost Sharing , Drug Industry , Fees, Pharmaceutical , Female , Health Expenditures/statistics & numerical data , Health Services Accessibility , Humans , Male , Managed Care Programs/economics , Medication Adherence , Pandemics , Pharmaceutical Services/economics , Retrospective Studies , SARS-CoV-2 , Socioeconomic Factors , United States/epidemiology
19.
Value Health Reg Issues ; 24: 240-246, 2021 May.
Article in English | MEDLINE | ID: covidwho-1199117

ABSTRACT

OBJECTIVES: Vaccines are recognized as the most effective strategy for long-term prevention of coronavirus disease 2019 (COVID-19) because they can reduce morbidity and mortality. The purpose of the present study was to evaluate willingness to pay (WTP) for a future COVID-19 vaccination among young adults in Southern Vietnam. METHODS: A cross-sectional, descriptive, and analytic study was undertaken with data collected from a community-based survey in southern Vietnam for 2 weeks in May 2020. The contingent valuation method was used to estimate WTP for COVID-19 vaccine. The average amount that respondents were willing to pay for the vaccine was US$ 85.9 2 ± 69.01. RESULTS: We also found the differences in WTP according to sex, living area, monthly income, and the level of self-rated risk of COVID-19. CONCLUSION: Our findings possibly contribute to the implementation of a pricing policy when the COVID-19 vaccine is introduced in Vietnam.


Subject(s)
COVID-19 Vaccines/economics , Health Expenditures/standards , Immunization/economics , Patients/psychology , Adolescent , Adult , COVID-19 Vaccines/therapeutic use , Cross-Sectional Studies , Female , Health Care Costs/standards , Health Care Costs/statistics & numerical data , Health Expenditures/statistics & numerical data , Humans , Immunization/methods , Male , Middle Aged , Patients/statistics & numerical data , Vietnam
20.
Hosp Pract (1995) ; 49(4): 232-239, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1191854

ABSTRACT

The COVID-19 pandemic caused the United States to hit record numbers of COVID-19 cases: peak unemployment of 14.7%, an increase in $4 trillion in national debt, and an estimated 3.4% GDP decline. The current socio-economic environment the pandemic created is just an earthquake that can create a tsunami that is bound to hit the healthcare system and can be felt around the globe. This tsunami is composed of a post-pandemic increase in healthcare facilities admission of indigent patients, decrease in medical reimbursement, and high operating costs to maintain healthcare workers, which can cause a synergistic effect that can lead to healthcare facilities experiencing significant negative total revenue. Time is of the essence, and it is imperative to make a collective effort from all healthcare professionals and legislatures to shift the nation's attention to the issue at hand that can threaten the closure of many healthcare facilities post-pandemic.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Health Care Sector/organization & administration , COVID-19/prevention & control , COVID-19 Vaccines/supply & distribution , Communicable Disease Control/economics , Economic Recession/statistics & numerical data , Health Care Sector/economics , Health Expenditures/statistics & numerical data , Humans , Pandemics , Poverty , SARS-CoV-2 , Socioeconomic Factors , United States/epidemiology
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