Preparing for COVID-19 in Low- and Middle-Income Countries: Leveraging U.S. Global Health Assets

Authors: Jennifer Kates, Kellie Moss, and Stephanie Oum

Published: Apr 24, 2020

As the COVID-19 pandemic continues to worsen, there is growing concern about its impact in low- and middle-income countries (LMICs), particularly those in sub-Saharan Africa, home to more than one billion people. Cases have now been reported in many LMICs and are rising rapidly in some. Should coronavirus spread even more significantly in sub-Saharan Africa and elsewhere, the impact could be extreme, given already low-resourced health systems and multiple other health challenges.

The United States has begun to respond, with Congress appropriating more than $2 billion in emergency funding to address COVID-19 in LMICs and the State Department and USAID providing resources to a growing list of countries. This data note updates an earlier analysis that examined where the U.S. government already had existing global health assets that could be mobilized to address COVID-19. It includes the latest case numbers, expands beyond health to identify countries where the U.S. also provides (non-health) development assistance, and identifies the most recent list of countries receiving COVID-19 assistance from the U.S. In addition, it indicates the level of COVID-19 preparedness in each country, based on the World Health Organization’s country preparedness and readiness assessment index (which rates countries from Level 1, least prepared to Level 5, most prepared).

Table 1 provides a regional summary of where the U.S. supports global health programs and/or provides COVID-19 assistance. Table 2 provides a full list of all countries identified, including reported cases of COVID-19 (as of April 21, 2020), total bilateral U.S. funding for global health programs, country-level U.S. COVID-19 funding, and COVID-19 preparedness capacity level.¹ It also indicates whether the U.S. provides other (non-health) development assistance. Key findings are as follows:

There are a total of 108 countries that received either U.S. bilateral global health assistance in FY 2018 (64 countries) and/or are receiving U.S. COVID-19 assistance (102 countries).²
Almost all of these countries (105 of 108) had reported COVID-19 cases as of April 21, 2020.
Of the 102 countries receiving U.S. COVID-19 funding, 52 are also countries that received U.S. global health assistance in FY 2018.
Almost all of these countries (103 of 108) receive other, non-health, U.S. development assistance, which may include assistance for agriculture, education, peace and security, etc.
Most of the countries receiving U.S. assistance are in Africa, followed by Asia and Latin America and the Caribbean. Most of the reported cases in these countries are in Latin America and the Caribbean, followed by South/Central Asia.
PEPFAR and the maternal and child health program, respectively, operate in the greatest number of countries (38 each), followed by the family planning and reproductive health program (36). Most countries (over 90%) in each of these program areas are also receiving U.S. COVID-19 funding.
Of the 107 countries assigned a COVID-19 preparedness capacity level by WHO, almost all (94) are at Level 3 or below, with more than half (57) designated as Level 3 (capacity is ≤60%), a third (36) as Level 2 (capacity is ≤40%), and one as Level 1 (capacity is ≤20%). The remaining 13 include 11 countries designated as Level 4 (capacity is ≤80%) and two designated as Level 5 (capacity is >80%).

Overall, we found much overlap between the countries being provided with COVID-19 support by the U.S. and those with existing U.S. global health assets, suggesting an opportunity to leverage longstanding programs and expertise. At the same time, however, it is highly likely that many other LMIC countries not currently receiving U.S. COVID-19 funding will experience growing numbers of cases and require enhanced assistance. Further, it remains unclear how ongoing U.S. global health efforts will be impacted, including whether some will be scaled back as resources, including personnel, are diverted to address COVID-19 and/or if country health systems become overwhelmed by COVID-19, undermining their ability to continue. Through two of the three emergency supplemental bills for the coronavirus response passed thus far, the U.S. has allocated approximately $2.4 billion in funding specifically for U.S. global health and other assistance for the global outbreak, although it is likely that additional funding will be needed. Still, given the need for the U.S. (and other donors) to confront their own COVID-19 epidemics, any additional foreign assistance may face stiff competition with growing domestic demands, including for stimulating the economy.

Table 1: COVID-19 in Countries Receiving Bilateral U.S. Global Health and/or COVID-19 Assistance, by Region
Region	Countries Receiving U.S. Global Health and/or COVID-19 Assistance
	# of Countries:					# of COVID-19 Cases(as of 4/21/20)
	Overall	Receiving U.S. Global Health Assistance	Receiving U.S. COVID-19 Assistance	With COVID-19 Cases (as of 4/21/20)	With Low COVID-19 Preparedness Capacity Level(Levels 1-3)*	# of COVID-19 Cases(as of 4/21/20)
Africa	45	34	41	44	42	24,088
East Asia/Pacific	11	10	11	11	8	22,621
Europe/Eurasia	17	1	17	17	11	321,552
Latin America/Caribbean	17	9	15	17	15	96,420
Near East	6	2	6	6	6	3,216
South/Central Asia	12	8	12	10	12	38,741
TOTAL	108	64	102	105	94	506,638
NOTES: Reflects countries receiving bilateral assistance through USAID and the Department of State in FY 2018 except NTD countries, which reflect information available on Nov. 13, 2019, and GHS countries, which reflect FY 2019 presence. Reflects all countries receiving COVID-19 assistance from the U.S. government as announced as of April 16, 2020 and includes some high-income countries. * WHO-designated level of a country’s ability to respond to COVID-19 based on capacity and risk assessments: Level 1 indicates that the country’s health system is ≤20% prepared, Level 2 is ≤40%, Level 3 is ≤60%, Level 4 is ≤80%, and Level 5 is >80%. # of cases includes confirmed and presumed positive cases.SOURCES: KFF analysis of data from U.S. Foreign Assistance Dashboard (foreignassistance.gov, accessed 11-12-19); USAID NTD Program website (NeglectedDiseases.gov, as of 11-13-19); KFF personal communication with USAID, 12-19; U.S. Department of State, “UPDATE: The United States Is Continuing to Lead the Humanitarian and Health Assistance Response to COVID-19,” fact sheet, April 16, 2020; WHO, COVID-19 Strategic Preparedness and Response Plan: Country Preparedness and Response Status for COVID-19 as of 13 April 2020; Johns Hopkins University, Coronavirus Resource Center, accessed: https://coronavirus.jhu.edu/map.html, as of April 21, 2020.

Table 2: COVID-19 in Countries Receiving Bilateral U.S. Global Health and/or COVID-19 Assistance

Region

Country

# of COVID-19 Cases (as of 4/21/20)

U.S. Global Health Assistance(FY 2018^)

# of U.S. Global Health Programs

Global Health Program Presence*

U.S. COVID-19 Assistance^(as of 4/16/20)

COVID-19 Preparedness Capacity Level⁺

Recipient of Other U.S. Foreign Assistance(FY 2018)

Family Planning/ Reproductive Health

PEPFAR (HIV)

Malaria

Maternal & Child Health

Nutrition

Tuberculosis

Global Health Security

NTDs/Other PH Threats

South/Central Asia

Afghanistan

1,092

$39,500,000

–

$18,000,000

Europe/Eurasia

Albania

609

—

–

$1,200,000

Africa

Algeria

2,811

—

–

$500,000

Africa

Angola

$37,418,000

–

$570,000

Europe/Eurasia

Armenia

1,401

—

–

$1,700,000

Europe/Eurasia

Azerbaijan

1,480

—

–

$1,700,000

South/Central Asia

Bangladesh

3,382

$66,000,000

–

$9,600,000

Europe/Eurasia

Belarus

6,723

—

–

$1,300,000

Latin America/Caribbean

Belize

—

–

$300,000

Africa

Benin

$24,000,000

–

—

South/Central Asia

Bhutan

—

–

$500,000

–

Latin America/Caribbean

Bolivia

598

—

–

$750,000

–

Europe/Eurasia

Bosnia and Herzegovina

1,342

—

–

$1,200,000

Africa

Botswana

$64,764,000

–

$1,500,000

Latin America/Caribbean

Brazil

43,079

$300,000

–

—

Europe/Eurasia

Bulgaria

975

—

–

$500,000

Africa

Burkina Faso

600

$29,000,000

–

$6,000,000

East Asia/Pacific

Burma

121

$30,482,000

–

$7,300,000

Africa

Burundi

$31,340,000

–

—

East Asia/Pacific

Cambodia

122

$33,911,000

$4,000,000

Africa

Cameroon

1,163

$63,674,000

–

$8,000,000

Africa

Central African Republic

—

–

$5,200,000

Africa

Chad

—

–

$3,000,000

Latin America/Caribbean

Colombia

4,149

—

–

$10,400,000

Africa

Côte d’Ivoire

916

$135,876,000

–

$1,600,000

Africa

Dem. Rep. of the Congo

350

$181,458,000

$14,400,000

Africa

Djibouti

945

$300,000

–

$500,000

Latin America/Caribbean

Dominican Republic

5,044

$13,901,000

–

$1,400,000

Latin America/Caribbean

Ecuador

10,398

—

–

$2,000,000

Africa

Egypt

3,490

$10,050,000

–

—

Latin America/Caribbean

El Salvador

225

—

–

$2,600,000

Africa

Eswatini

$65,768,000

–

$750,000

Africa

Ethiopia

114

$204,363,000

$9,000,000

Europe/Eurasia

Georgia

408

—

–

$1,700,000

Africa

Ghana

1,042

$68,233,000

–

$1,600,000

Europe/Eurasia

Greece

2,401

—

–

$500,000

Latin America/Caribbean

Guatemala

294

$13,000,000

–

$2,400,000

Africa

Guinea

688

$23,000,000

–

$500,000

Latin America/Caribbean

Guyana

—

–

—

Latin America/Caribbean

Haiti

$123,586,000

–

$13,200,000

Latin America/Caribbean

Honduras

494

—

–

$2,400,000

South/Central Asia

India

20,080

$66,216,000

–

$5,900,000

East Asia/Pacific

Indonesia

7,135

$35,962,000

–

$5,000,000

Near East

Iraq

1,602

—

–

$25,600,000

Europe/Eurasia

Italy

183,957

—

–

$50,000,000

–

Latin America/Caribbean

Jamaica

223

—

–

$700,000

Near East

Jordan

428

$21,000,000

–

$8,000,000

South/Central Asia

Kazakhstan

1,995

—

–

$1,600,000

Africa

Kenya

296

$523,112,000

–

$4,500,000

Europe/Eurasia

Kosovo

510

—

–

$1,100,000

—

South/Central Asia

Kyrgyzstan

590

$4,300,000

–

$900,000

East Asia/Pacific

Laos

$3,500,000

–

$3,500,000

Near East

Lebanon

677

—

–

$13,300,000

Africa

Lesotho

–

$79,574,000

–

$750,000

Africa

Liberia

101

$34,500,000

–

$1,000,000

Africa

Libya

—

–

$6,000,000

Africa

Madagascar

121

$52,000,000

–

$2,500,000

Africa

Malawi

$194,893,000

–

$4,500,000

East Asia/Pacific

Malaysia

5,482

—

–

$200,000

Africa

Mali

258

$66,300,000

–

$5,700,000

Africa

Mauritania

—

–

$250,000

Africa

Mauritius

328

—

–

$500,000

Latin America/Caribbean

Mexico

8,772

—

–

$500,000

Europe/Eurasia

Moldova

2,614

—

–

$1,200,000

East Asia/Pacific

Mongolia

—

–

$1,200,000

Europe/Eurasia

Montenegro

313

—

–

$300,000

Africa

Morocco

3,209

—

–

$1,700,000

Africa

Mozambique

$427,685,000

–

$5,800,000

Africa

Namibia

$66,879,000

–

$750,000

South/Central Asia

Nepal

$43,866,000

–

$1,800,000

Africa

Niger

657

$22,000,000

–

$4,000,000

Africa

Nigeria

665

$398,561,000

–

$21,400,000

Europe/Eurasia

North Macedonia

1,231

—

–

$1,100,000

–

South/Central Asia

Pakistan

9,565

$7,000,000

–

$9,400,000

Latin America/Caribbean

Panama

4,658

—

–

$750,000

East Asia/Pacific

Papua New Guinea

$5,474,000

–

$1,200,000

Latin America/Caribbean

Paraguay

208

—

–

$1,300,000

Latin America/Caribbean

Peru

17,837

—

–

$2,500,000

East Asia/Pacific

Philippines

6,599

$28,500,000

–

$6,000,000

Africa

Republic of Congo

165

—

–

$250,000

Europe/Eurasia

Romania

9,242

—

–

$800,000

Africa

Rwanda

150

$114,591,000

–

$2,000,000

Africa

Senegal

412

$59,035,000

–

$3,900,000

Europe/Eurasia

Serbia

6,630

—

–

$1,200,000

Africa

Sierra Leone

$19,500,000

–

$400,000

Africa

Somalia

286

—

–

$12,500,000

Africa

South Africa

3,465

$580,905,000

–

$8,400,000

Africa

South Sudan

$36,545,000

–

$13,100,000

South/Central Asia

Sri Lanka

310

—

–

$1,300,000

Africa

Sudan

107

—

–

$13,700,000

Near East

Syria

—

–

$18,000,000

–

South/Central Asia

Tajikistan

–

$7,750,000

–

$866,000

Africa

Tanzania

254

$480,839,000

$1,400,000

East Asia/Pacific

Thailand

2,811

—

–

$2,700,000

East Asia/Pacific

Timor-Leste

$2,000,000

–

$1,100,000

Africa

Togo

—

–

—

Africa

Tunisia

884

—

–

$600,000

Europe/Eurasia

Turkey

95,591

—

–

$800,000

South/Central Asia

Turkmenistan

–

—

–

$920,000

Africa

Uganda

$463,396,000

$3,600,000

Europe/Eurasia

Ukraine

6,125

$34,644,000

–

$9,100,000

South/Central Asia

Uzbekistan

1,678

$4,000,000

–

$848,000

Latin America/Caribbean

Venezuela

285

—

–

$9,000,000

East Asia/Pacific

Vietnam

268

$31,349,000

–

$4,500,000

Near East

West Bank/ Gaza

466

—

–

$5,000,000

Near East

Yemen

$3,500,000

–

$500,000

Africa

Zambia

$415,961,000

–

$3,400,000

Africa

Zimbabwe

$170,107,000

–

$3,000,000

Number of Countries

108

506,638cases

$5,765,368,000

$463,054,000(102 countries)

107

103

NOTES: # of cases includes confirmed and presumed positive cases. ^ Total does not include some country-level funding (i.e., NTD and global health security program funding) that is not available on ForeignAssistance.gov; it also does not include funding for regional or “worldwide” program efforts that may reach additional countries. NTDs are neglected tropical diseases. * X indicates country receiving bilateral assistance through USAID and the Department of State in FY 2018 except NTD countries, which reflect information available on Nov. 13, 2019, and GHS countries, which reflect FY 2019 presence. ^ U.S. COVID-19 Assistance reflects all countries receiving COVID-19 assistance from the U.S. government and includes some high-income countries. Does not include funding specified for regional programs that may reach additional countries. + The COVID-19 Preparedness Capacity Level indicates the WHO-designated level of that country’s ability to respond to COVID-19 based on capacity and risk assessments: Level 1 indicates that the country’s health system is ≤20% prepared, Level 2 is ≤40%, Level 3 is ≤60%, Level 4 is ≤80%, and Level 5 is >80%. “–” indicates no cases have been reported, country-level funding is not specified for this country, country does not have a USG program that is supported by country-level funding available on ForeignAssistance.gov, or COVID-19 preparedness capacity level not specified for country.SOURCES: Johns Hopkins University, Coronavirus Resource Center, accessed: https://coronavirus.jhu.edu/map.html, as of April 21, 2020; KFF analysis of data from U.S. Foreign Assistance Dashboard (foreignassistance.gov, accessed 11-12-19); USAID NTD Program website (NeglectedDiseases.gov, as of 11-13-19); KFF personal communication with USAID, 12-19; U.S. Department of State, “UPDATE: The United States Is Continuing to Lead the Humanitarian and Health Assistance Response to COVID-19,” fact sheet, April 16, 2020; WHO, COVID-19 Strategic Preparedness and Response Plan: Country Preparedness and Response Status for COVID-19 as of 13 April 2020; KFF analysis of data from U.S. Foreign Assistance Dashboard (foreignassistance.gov, accessed April 6, 2020).

The preparedness and response categorization (Levels 1-5) is based on the country’s 1) operational readiness capacities based on the International Health Regulations (IHR) State Parties Annual Reporting self-assessment tool and 2) position on a continuum of response scenarios including country preparedness, risk of imported cases, number of imported cases, and localized and community transmission. This categorization will be updated regularly based on each country’s COVID-19 situation as the epidemic evolves. Level 1 indicates that the country’s health system is ≤20% prepared; Level 2 is ≤40%; Level 3 is ≤60%; Level 4 is ≤80%; and Level 5 is >80%. WHO, COVID-19 Strategic Preparedness and Response Plan: Country Preparedness and Response Status for COVID-19 as of 30 March 2020. ↩︎
Two of the 102 countries receiving U.S. COVID-19 assistance are high-income countries that are facing particularly significant outbreaks – Greece and Italy. ↩︎

News Release

Poll: Just Over Half of the Nation’s Workforce Have Lost a Job or Income Due to Coronavirus, though Most of Them Believe They Will Get Their Job and Income Back Within Six Months

Most of the Public Overall Say Worry and Stress Related to the Pandemic is Causing Ill Effects, Including Lost Sleep, Loss of Appetite or Overeating, and Difficulty Controlling Temper

Published: Apr 24, 2020

With many businesses shut down and job losses mounting nationwide, just over half of the nation’s workers (55%) now say they have lost a job or had their incomes reduced as a result of the health and economic crises sparked by the novel coronavirus pandemic, the latest KFF Health Tracking poll finds.

This includes a quarter (26%) of all those who were working on Feb. 1 who say they lost a job and another 7% who say they got furloughed, as well as those who say their work hours have been reduced (21%) or that they have had to take a pay cut (13%).

Among those who were working part-time in February, three-quarters (76%) report a lost job or income. The shares are also higher-than-average among those who are paid by the hour or the job (68%) and those with low (65%) or moderate (62%) incomes.

In addition to those who personally experienced a coronavirus-related job or income loss, 16% say they have a spouse or partner who has lost their job, had hours reduced, took pay cut, or been furloughed. In total, 42% of all U.S. adults say they or their spouse or partner has been affected in such ways.

For many, the loss of a job and income is affecting the ability to keep up with their bills.

Among affected workers, more than a third (36%) say they have fallen behind in paying their credit card and other bills. About a quarter say they have had problems paying their utility bills (27%) or fallen behind on their rent or mortgage (25%), and one in five report problems paying for food (21%). Smaller shares say they have had trouble affording health insurance (13%) or prescription drugs (5%).

“The economy right now is crushing workers, and many are piling up bills and debts,” KFF President and CEO Drew Altman said. “Their optimism that things will turn around by the Fall may be wishful thinking.”

Nearly half (46%) of those in households that had a job or income loss since February say they have experienced at least one of those financial difficulties since then.

Among the public at large, three in 10 (31%) report at least one of those financial difficulties as a result of the coronavirus pandemic. Those most likely to report such problem include black (57%) and Hispanic (42%) adults, those with low annual incomes at or below $40,000 (40%), and younger adults ages 18-29 (39%).

Most Affected Workers Expect Their Situations to Improve Within the Next Six Months

In spite of these job setbacks and financial troubles, affected workers are mostly optimistic that things will return to normal for them this year. Eight in 10 (83%) of those affected say they expect to be hired back, return to their previous salary or income, or get their hours back in the next six months.

Less than half of those who lost their job or were laid off say their former place of employment is still open for business (39%) while most say it is closed temporarily (56%). Just 2% say their former place of employment has closed permanently.

Worry and Stress Related to Coronavirus are Affecting Most People’s Mental and Physical Health

Among the public overall, more than half (56%) say that worry and stress related to the coronavirus situation are leaving some marks on their mental and physical well-being.

This includes four in 10 who cite problems with their sleep and one in three who report overeating or a loss of appetite. Smaller shares say coronavirus worry or stress is causing frequent headaches or stomachaches (18%), difficulty controlling their temper (15%), increased alcohol or drug use (13%), or worsening chronic health conditions (9%).

The coronavirus outbreak’s impact on mental health and well-being also seems to be affecting a larger share of those who experienced a loss of employment income.

Two-thirds of those who say they or their spouse have lost their job or income (65%) say that worry or stress related to the coronavirus outbreak has caused them to experience at least one adverse effect on their mental or physical health in the past two months.

Designed and analyzed by public opinion researchers at KFF, the poll was conducted April 15-20, 2020 among a nationally representative random digit dial telephone sample of 1,202 adults. Interviews were conducted in English and Spanish by landline (261) and cell phone (941). The margin of sampling error is plus or minus 3 percentage points for the full sample. For results based on subgroups, the margin of sampling error may be higher.

Contact

Rakesh Singh

KFF

rsingh@kff.org

Contact

Craig Palosky

KFF

cpalosky@kff.org

KFF Health Tracking Poll – Late April 2020: Coronavirus, Social Distancing, and Contact Tracing

Authors: Ashley Kirzinger, Liz Hamel, Cailey Muñana, Audrey Kearney, and Mollyann Brodie

Published: Apr 24, 2020

Section:

0 / 0

Social Distancing And Contact Tracing

Key Findings:

The latest KFF Health Tracking Poll conducted April 15-20^th finds about half the public (51%) now saying that when it comes to the coronavirus outbreak in the U.S., “the worst is yet to come.” This is down 23 percentage points from the share who said the same (74%) in the Early April KFF Health Tracking Poll conducted less than three weeks ago. Majorities of Democrats (64%) and independents (56%) continue to say “the worst is yet to come,” but now twice as many Republicans say “the worst is behind us” (53%) as say “the worst is yet to come” (27%).
While an increasing share of the public now say the worst of the outbreak is behind us, most Americans (80%) say strict shelter-in-place measures are worth it in order to protect people and limit the spread of coronavirus. Fewer (19%) say the strict shelter-in-place measures are placing unnecessary burdens on people and the economy and causing more harm than good. Majorities also say they can continue following strict social distancing and shelter-in-place guidelines for more than another month while less than one in five say they either cannot follow the guidelines at all (3%) or say they can follow the guidelines for less than a month (14%). While a majority of Republicans say they can follow social distancing guidelines for a month or longer, three in ten Republicans say they can either follow them “less than one month” or “not at all.”
The severity of an outbreak in one’s area appears to be related to behaviors around visiting friends and family outside the home. Among those living in counties with 5 or fewer COVID-19 deaths, 36% say they have left their home at least once in the last week to visit with friends or family, compared with 21% of those living in counties with more than 25 deaths.
The vast majority of adults (84%) say their lives have been disrupted at least some by the coronavirus outbreak in the U.S. This is up 12 percentage points from the KFF Health Tracking Poll conducted March 25-30 and up 44 percentage points from the March 11-15 poll.
While the public is divided in their willingness to download apps for their phones as a way to trace and monitor the contacts of people infected with coronavirus, support increases to as high as 66% once people hear the argument that this could allow for many schools and businesses to reopen. Republicans are less willing to download and use a contact tracing app than Democrats and independents.

Over the past two months, the coronavirus outbreak has drastically changed the way of life in the U.S. With nearly 800,000 reported cases of the novel coronavirus in the U.S. and more than 42,000 reported deaths, many in the country are wondering when, if ever, normal life will return. When coronavirus led to the closing of schools and businesses in early March, the KFF March Tracking Poll found four in ten adults already reporting that their lives had been disrupted either “a lot” or “some” as a result of the virus. Now, five weeks later, the latest KFF Health Tracking Poll finds the share who say their life has been disrupted has more than doubled to 84%. This includes large majorities across race and ethnicity, gender, and parental status.

Figure 1: Increasing Shares Of Americans – Across Demographic Groups –Say Their Lives Have Been Disrupted By The Coronavirus Outbreak

At least eight in ten Republicans (82%), independents (81%), and Democrats (89%) now say their lives have been disrupted. This is more than a 50 percentage point increase from the far smaller share of Republicans (30%) who initially reported disruption back in early March, and a larger share of both independents (up 41 percentage points) and Democrat (up 40 percentage points) also now report their lives have been disrupted at least some by coronavirus.

Partisans Differ On U.S. Future Outlook and Support For Shelter-In-Place Measures

While the partisan divide on whether their lives have been disrupted by coronavirus has largely dissipated, there are now substantial partisan differences in people’s outlook on how coronavirus will impact the U.S. in the coming months.

In late March, a majority of the public said that when it came to the coronavirus outbreak in the U.S., “the worst was yet to come” (74%), while few (13%) said the “worst is behind us.” This included majorities of Democrats (82%), independents (77%), and Republicans (66%) saying “the worst is yet to come.” The latest KFF Tracking Poll now finds about half of the public (51%) saying the “worst is yet to come,” down 23 percentage points in less than three weeks. Majorities of Democrats (64%) and independents (56%) continue to say the “worst is yet to come,” but now one-fourth (27%) of Republicans say “the worst is yet to come” (down 39 percentage points from the March 25-30 KFF Poll). A slight majority (53%) of Republicans say “the worst is behind us,” while far fewer, but still twice as many who said this less than three weeks ago, independents (31%) and Democrats (21%) now say the same.

Figure 2: Partisans Now Divided On Whether The Worst Of Coronavirus Is Behind Us Or If The Worst Is Yet To Come

This partisan divide may reflect the mixed messages from governors, President Trump, and public health officials. While public health officials are warning about the possible impacts of relaxing social distancing measures, three Republican governors announced this week that some non-essential businesses will be allowed to re-open.

Despite this, a large majority of adults (80%) say strict shelter-in-place measures are worth it in order to protect people and limit the spread of coronavirus. Fewer (19%) say the strict shelter-in-place measures are placing unnecessary burdens on people and the economy and causing more harm than good. While majorities across partisans say these measures are worth it, a larger share of Republicans (38%) compared with independents (16%) and Democrats (5%) say they are causing more harm than good.

Figure 3: Majorities Across Partisans Say Shelter-In-Place Measures Are Worth It To Protect People

Some Republican governors have been among the strongest voices for re-opening businesses and lifting strict shelter-in-place orders sooner rather than later. However, even among Republicans living in states with Republican governors, a larger share say the strict measures are worth it (63%) than say they are causing more harm than good (36%).

Table 1: Majorities Across Partisans Living In Republican-Led State Support Strict Shelter-in-place Measures
Which comes closest to your view?	Living in a state with a Republican Governor
Which comes closest to your view?	Total	Democrats	Independents	Republicans
Strict shelter-in-place measures are worth it in order to protect people and limit the spread of coronavirus	79%	96%	83%	63%
Strict shelter-in-place measures are placing unnecessary burdens on people and the economy and are causing more harm than good	20	4	16	36

Residents in most parts of the U.S. have been engaging in social distancing or sheltering-in-place for several weeks and all states, including Washington D.C., have issued mandates either restricting businesses, travel, or the activities of their residents. To see a complete list, see State Data and Policy Actions to Address Coronavirus.

Most adults (84%) say they have been sheltering-in-place because of the coronavirus outbreak and only leaving their home for essential services such as food, medicine, and health care – a similar share who reported this in the KFF Early April Tracking Poll (82%). Three-fourth of adults (76%) say they have bought or made a protective mask to wear in public, consistent with many social distancing guidelines issued by states. Those living in counties where more than 25 people have died from COVID-19 are more likely than those in counties with 5 or fewer deaths to report using a mask (82% vs. 69%).¹

On April 16^th President Trump announced guidelines for states to begin lifting shelter-in-place and social distancing restrictions in order to reopen businesses and get the economy going again² but many states have announced that it could be weeks or even months until many shelter-in-place restrictions are lifted. With all of this uncertainty, the majority of Americans say they can continue following strict social distancing and shelter-in-place guidelines in order to limit the spread of coronavirus in their communities.

Eight in ten say they can follow strict social distancing and shelter-in-place guidelines for more than another month, including 37% who say they can follow the guidelines between 1 and 3 months, and one in ten who say they can follow them between 4 and 6 months. An additional one-third (34%) say they can follow them for more than 6 months longer, indicating there is a group of Americans who are willing to follow the guidelines as long as it takes to limit the spread of coronavirus.

Figure 4: Most Say They Can Follow Strict Social Distancing Guidelines For Longer Than Another Month

On the other hand, there is a segment of the population who either isn’t willing to social distance for much longer or even at all. Nearly one-fifth say they can follow these strict guidelines for less than a month (14%) or say they are not willing to do it any longer (3%).

Majorities of Democrats (92%), independents (80%), and Republicans (68%) all say they can follow strict social distancing and sheltering-in-place guidelines for a month or longer, though Republicans are less likely than others to say they can sustain this practice for more than three months (31% compared to half of Democrats and 44% of independents).Three in ten Republicans say they can only follow strict social distancing and sheltering-in-place guidelines for less than another month (compared to seven percent of Democrats and 17% of independents).

Figure 5: Partisans On How Long They Say They Can Keep Up Social Distancing Guidelines

With all states issuing either shelter-in-place or some social distancing restrictions (i.e., restricting the size of gatherings or closing schools), the majority of adults say they have not left their home at all during the last week either to visit close friends or family (70%), go to work (67%), or exercise (57%). About one in five (20%) say they haven’t left their home to shop for food, medicine, or essential household items but an additional 36% say they have only left their home once to do this.

Figure 6: Most Say They Have Left Home Very Little Over Past Week To Exercise, Visit Family, Go To Work, Or Shop For Food

A small share (10%) of adults say they have left their home 4 or more times in the past week to either shop for food, medicine, or essential household items, or to visit close friends and family – activities that all leaders are encouraging people, even those who are essential workers, not to engage in or at least not often. This includes 7% of adults 60 and older who either have a chronic condition themselves or lives in a household with someone who has one, a group most at-risk for complications of coronavirus, who say they have left their home 4 or more times.

The severity of an outbreak in one’s area appears to be related to behaviors around visiting friends and family outside the home. Among those living in counties with 5 or fewer COVID-19 deaths, 36% say they have left their home at least once in the last week to visit with friends or family, compared with 21% of those living in counties with more than 25 deaths.

Essential Workers

While one-fifth of Americans say they have left their home 4 or more times in the past week in order to go to work, this is largely driven by those who have been deemed “an essential worker.” One-third (34%) of all adults say they have been deemed an “essential worker,” meaning they are still required to work outside their home during the coronavirus outbreak in the U.S. Three in ten adults earning less than $40,000 annually say they are essential workers compared to about four in ten of those earning between $40,000 and $89,999 (41%) or $90,000 or more annually (36%). A substantial share of essential workers (39%) say they or someone in their household has a serious health condition that could put them at higher risk of complications from COVID-19 and 13% of essential workers are over the age of 60.

Overall, most adults give themselves and the people in their household either an “A, excellent” for following the social distancing guidelines in their area over the past two weeks (53% and 52%, respectively) or a “B, good” (37% and 31%, respectively). A smaller share, but still a majority, give their neighbors or people who live near them either an “A” (35%) or a “B” (35%).

Figure 7: People Say They And Their Household Members Are Doing Better At Social Distancing Than Their Neighbors

Contact Tracing and Testing

One of the key elements in any plan to reopen businesses and restart the U.S. economy is widespread contact tracing, which is the ability to trace and monitor the contacts of infected people.³ One way to track the spread of coronavirus in communities is through people’s smart phones. Using smart phones for contact tracing is being widely discussed by public health officials and experts The latest KFF Health Tracking Poll finds the public divided on whether they would be willing to download an app for these purposes, but support increases once people hear the argument that this could help allow for many aspects of daily life to resume. Yet, the public is largely skeptical of the role of tech companies in managing this data and would like to see the data controlled by public health agencies.

Overall, a majority of the public (68%), are willing to use an app on their phone to share results from a coronavirus test in order to allow public health officials to track the spread of the outbreak. Majorities across age groups and party identification, say they are willing to use an app for these purposes.

Figure 8: Majorities Willing To Share Test Results Using Smart Phone App

The public is more divided in their willingness to download an app for their phone that uses Bluetooth and GPS technology to track who they come into close contact with. Half of the public are “willing” to download an app that alerts them if they have come in contact with someone who has tested positive for coronavirus so that they can take steps to protect them and their family while a similar share (47%) say they are “unwilling” to download such an app. The public are somewhat more “unwilling” than “willing” to download the app if they are told the app would provide information to public health officials in order to track the spread of coronavirus (53% who say they are “unwilling” to download the app compared to 45% who are “willing”).

Figure 9: Many Are Unwilling To Download App That Provides Information To Public Health Officials To Track Coronavirus, Or Alert Them If They Have Come Into Contact With Someone Who Has Tested Positive

There are large differences across age groups and partisans in willingness to download contact tracing apps. While slightly more than half of 18-29 year olds saying they are willing to download an app that provides information to public health officials (53%) or alerts them if they have come into contact with someone who has tested positive for coronavirus (55%), smaller shares of older adults (65 and older) say they are willing to download an app for either of these purposes (36% and 42%, respectively).

Additionally, while about one-third of Republicans saying they are “willing” to download an app to help public health officials track the spread of the outbreak (29%) or alerts them if they have come into contact with someone who tested positive for coronavirus (35%), majorities of Democrats say they are “willing” to download an app for any of the mentioned contact tracing purposes (63% and 58%, respectively).

Figure 10: Republicans And Older Adults Less Willing To Download Contact Tracing Apps

Some attitudes Change After Hearing Messages About Contact Tracing Apps

While initially divided on whether they would download an app for contact tracing, some may be willing to download the app once they hear that it would allow for many aspects of daily life to get back to normal. Two-thirds are willing to download and use the app once they hear that using such an app could allow more people to go back to work or school (66%) or that it would allow many businesses to re-open and start up the economy (66%). A smaller share, but still a majority, are willing to download the app once they hear that the app would give them information so they can talk to their doctor about what to do if they come into close contact with someone who has tested positive for coronavirus (62%) or that the data collection is “opt-in,” meaning people would have to actively choose to share the information (54%). About half (52%) are willing to download the app if the data collection is “opt-out,” meaning people would have to actively choose not to share information. Fewer are willing to download the app if they hear that they may get many alerts about being exposed to coronavirus, even if the exposure was only minor or the data could be potentially inaccurate (42%) or that there is a chance that data from the app could be hacked (28%).

Figure 11: Majority Willing To Download Contact Tracing App Once They Hear It Would Allow People To Go To Work And Businesses To Open

Who manages the data also affects peoples’ willingness to download an app for contact tracing. Six in ten saying they are “willing” to download an app to track the spread of coronavirus if data from the app was managed by their state health department (63%), the federal Centers for Disease Control and Prevention (62%), or their local health department (62%). Far fewer (31%) are willing to download the app if the data was managed by a private tech company.

Figure 12: Twice As Many Willing To Download Contact Tracing App If Data Is Managed By Public Health Agencies Rather Than Tech Companies

While majorities of Democrats and independents and about half of Republicans are willing to download a contract tracing app if the data was managed by the CDC or their state or local health department, fewer than four in ten Democrats (37%), one-third of independents, and one-fourth of Republicans say they are willing to download such an app if the data was managed by a private tech company.

Table 2: Who Partisans Trust To Manage Data From Contact Tracing Apps
Percent who say they would be willing to download an app to track the spread of coronavirus if data from the app was managed by:	Party ID
	Democrats	Independents	Republicans
Their state health department	80%	64%	46%
The federal Centers for Disease Control and Prevention (CDC)	75	64	47
Their local health department	78	63	48
A private tech company	37	33	26

This may be due to the fact that nearly four in ten (38%) say they are more worried private companies will sell their personal data from the app while one-third (33%) say they are more worried the federal government will use the data for purposes beyond tracking the spread of coronavirus. About one-fourth (22%) volunteer that they are equally worried about both of these things.

Figure 13: Four In Ten More Worried About Private Companies Selling Data While One-Third More Worried About Federal Government Misuse

Nearly two-thirds of the public say the use of contact tracing apps either wouldn’t have any impact on how safe they feel or say they would make them feel less safe. Half of the public (47%) say having public health officials track the spread of coronavirus using apps on people’s cell phones would have “no impact on how safe they feel,” while one-third (35%) say it would make them feel “safer” and one in five (17%) say it would make them feel “less safe.”

Figure 14: Nearly Two-Thirds Say Contact Tracing Apps Would Not Make Them Feel Safer

At-Home Testing

There has been discussion about the developments of a potential at-home testing kit for coronavirus with possible public availability. Seven in ten (72%) say they are either “very likely” or “somewhat likely” to use a coronavirus testing kit that they could do at their home and then send to a lab to find out if they have the virus. Unlike contact tracing apps, majorities across age groups and partisans say they are likely to use in-home testing kits.

Figure 15: Majorities Say They Are Likely To Use Home Testing Kit For Coronavirus

Personal Experience with Coronavirus

Four in ten now say they either they personally know someone who has “tested positive for coronavirus” (24%) or personally know someone who thinks they had or have coronavirus, even if they couldn’t get tested (29%). One in ten (9%) now say they personally know someone who has died from complications related to coronavirus.

Figure 16: Four In Ten Say They Know Someone Who Tested Positive Or May Have Had Coronavirus, One In Ten Know Someone Who Died

Knowing someone who has either died from complications from coronavirus, tested positive, or had coronavirus does not lead to larger support for using smart phone technology for contact tracing.

Table 3: Direct Experience With Coronavirus and Support for Contact Tracing Apps
Percent who say they would be willing to download/use an app for their phone …	Know someone who tested positive/had symptoms/died of coronavirus	Do not know someone who tested positive/had symptoms/died of coronavirus
… that tracks who you come into close contact with and then alerts you if you have come into contact with someone who tested positive for coronavirus so that you can take steps to protect you and your family	53%	49%
.. that tracks who you come into close contact with and then provides that information to public health officials to track the spread of coronavirus	49	42
… to share the results of your coronavirus test with public health officials in order for them to track the spread of the outbreak	68	68

Economic And Mental Health Impacts

Key Findings:

The latest KFF Health Tracking Poll finds just over half of the February workforce (35% of all adults) say they either have lost their job (26%), had hours reduced (21%), taken a pay cut (13%) or been furloughed (7%) as a result of the coronavirus outbreak. This includes three-fourths (76%) of those who were employed part-time, and about two-thirds of hourly or contract workers (68%), and workers from lower-income households (65% of those earning less than $40,000 annually).
Four in ten (42%) of all adults say that either they or their spouse or partner experienced a job loss or a cut in salary or hours due to the coronavirus. Among this group, most say the loss of income is either a “major problem” (41%) or “minor problem” (32%) for their household. The loss of income is a bigger problem for those in lower-income households with most of those earning less than $40,000 annually (58%) saying the loss of income is a “major problem” for their household.
Overall, three in ten adults (29%) say they have fallen behind in paying bills or had problems affording household expenses like food or health insurance coverage since February due to the coronavirus outbreak. This rises to about four in ten among younger adults ages 18-29, those with household incomes of $40,000 or less annually, and Hispanic adults. More than half (56%) of Black adults say they have had problems paying these bills in the last two months specifically because of coronavirus. Nearly half (46%) of those who had an income loss due to coronavirus say they have had difficulty paying bills or affording household expenses since the outbreak.
Many of the potential impacts of the loss of a job have not yet been fully realized by those affected by the coronavirus outbreak. Most say they have not yet applied for unemployment benefits (69%), and that they think that they will get their previous job or salary back within six months (83%). Less than half of those who lost their job or were laid off say their former place of employment is still open for business (39%) while most say it is closed temporarily (56%). Only 2% say their former place of employment has closed permanently.
Over half of U.S. adults (56%) report that worry or stress related to the coronavirus outbreak has caused them to experience at least one negative effect on their mental health and wellbeing, such as problems with sleeping or eating, increased alcohol use, or worsening chronic conditions. Stress and worry around the coronavirus also seems to be affecting larger shares of frontline health care workers and their families (64%) as well as those who experienced an income loss (65%).

The Economic Impact of Coronavirus on U.S. Families

Many U.S. adults say they have had difficulty paying household expenses because of the coronavirus outbreak. About one in five say they have fallen behind in paying credit card or other bills (21%), while one in seven have had problems paying their utilities (16%), fallen behind in paying their rent or mortgage (15%), or had problems paying for food (14%) as a result of the coronavirus outbreak. An additional one in ten say they have had problems affording their health coverage (7%) or their prescription medications (5%).

Figure 1: Significant Shares Say They Have Had Difficulty Paying Bills, Affording Household Expenses Due To Coronavirus Outbreak

Overall, three in ten adults say they have fallen behind in paying bills or had problems affording household expenses like food or health insurance coverage since February due to the coronavirus outbreak. This rises to four in ten among younger adults (18-29), those with household incomes of $40,000 or less annually, and Hispanic adults. More than half (57%) of Black adults say they have had problems paying these bills in the last two months.

Figure 2: Three In Ten Have Fallen Behind Paying Bills Or Had Problems Affording Health Insurance Or Other Household Expenses

Prior to the outbreak of coronavirus and the closing of non-essential businesses across the U.S., the February 2020 KFF Health Tracking Poll found about half of adults (52%) said they could pay a $500 unexpected bill in full at the time of service. Now, 2 months later, the share who say they could pay this bill in full at time of service is down 6 percentage points to 46%, with one in five (21%) now saying they would not be able to pay the bill at all and 28% saying they would put it on a credit card and pay it off over time or borrow money to cover the costs. Additionally, the share who say they wouldn’t be able to pay the bill at all has increased 6 percentage points over the past two months.

Figure 3: Fewer Now Report Being Able To Pay $500 Medical Bill

Large Shares Report Loss of Job or Wages

The U.S. Labor Department reported on April 23^rd that more than 26 million Americans have filed unemployment claims in the past four weeks, marking the greatest increase in unemployment in this century.¹ The latest KFF Health Tracking Poll finds just over half (55%) adults who were employed as of February 1, 2020 (63% of total) say they either have lost their job (26%), had hours reduced (21%), taken a pay cut (13%) or been furloughed (7%) as a result of the coronavirus outbreak. This equates to more than one-third (35%) of all U.S. adults who say they have lost their jobs or had a reduction in hours or pay as a result of the coronavirus outbreak.

Figure 4: Over Half Of February Workforce—Third Of All Adults—Report Loss Of Job, Hours Reduced, Or Pay Cuts Because Of Coronavirus

Three-fourths (76%) of those who were employed part-time as of February 1^st report either having their hours reduced, a pay cut, or a job loss because of the coronavirus outbreak, compared to 52% of those were employed full-time. Twice as many hourly or contract workers report loss of a job or income related to coronavirus compared to salaried workers (68% vs. 30%). Workers from lower-income households are also more likely to report having an employment-related loss due to coronavirus compared to households earning $90,000 or more annually.

Table 1: Many Of Those Who Were Employed As February 1 Now Report Employment-Related Loss Related To Coronavirus
	Total EmployedAs Of Feb. 1(66% of all adults)	Hours		Compensation		Household income
	Total EmployedAs Of Feb. 1(66% of all adults)	Full-time	Part-time	Salary	Hourly/By the job	<$40K	$40 to <$90K	$90K+
Percent who say they …
Had hours reduced	21%	20%	26%	13%	25%	23%	23%	17%
Lost job	26	23	41	9	34	31	30	13
Had to take a pay cut	13	13	15	9	15	13	14	14
Been placed on furloughed	7	6	8	3	8	10	5	3
Any of the above	55	52	76	30	68	65	62	38
NOTE: Individuals could say yes to both taken a pay cut or had hours reduced.

Figure 5: Four In Ten Say They Or Their Spouse/Partner Has Experienced Loss of Job Or Pay Cut Due To Coronavirus Outbreak

At least four in ten across most demographic groups, including more than half of Hispanic adults (56%), between the ages of 30 and 49 (53%), and those with household incomes between $40,000 and $89,999 (51%), report that either they or their spouse/partner have either lost their job, had hours reduced, took a pay cut, or have been furloughed as a result of the coronavirus outbreak. A smaller share of older adults, 65 and older, report a loss of employment-related income (18%).

Figure 6: Four In Ten Adults Say They Or Their Spouse Have Loss Job Or Had Their Pay Cut Due To Coronavirus Outbreak

Three in four (74%) of those who have experienced a loss of income (either themselves or a spouse) say it is a problem for their household including four in ten (41%) who say the loss of income is a “major problem” for their household. An additional one-third (32%) say the loss of income is a “minor problem.” About one-fourth say the loss of income is “not a problem” for their household.

Figure 7: Three-Fourths Say Loss Of Income Is A Problem

The loss of employment is a major problem for the lowest income households (less than $40,000 annual income), but a significant share of those living in households earning $40,000 to $89,999 also report the loss of income will be a problem for their household. Among those who say their household experienced a loss of job or income, six in ten (58%) of those earning less than $40,000 annually say the loss of income is a “major problem,” as do 39% of those earning between $40,000 and $89,999.

Figure 8: Those In Lower-Income Households Experiencing Job Or Income Loss More Likely To Say It Has Caused Problems

Nearly half (46%) of those who had an income loss due to coronavirus say they have had difficulty paying bills or affording household expenses since the outbreak (compared to three in ten among the public overall).

Figure 9: Nearly Half Of Those Who Have Had Their Income Impacted By Coronavirus Say They Have Experienced The Following

Most Who Have Experienced Employment Loss Say They Expect To Get Their Jobs Back

Among those who had lost their job or had their pay reduced as a result of the coronavirus (35% of the total population), eight in ten (83%) say they expect to be either hired back, get their previous salary or hours back in the next six months. This includes 86% of those who had a job loss or were furloughed who say they expect to be hired back and 81% of those who had a pay cut or their hours reduced who say they expect to get their previous salary or hours back in the next six months.

Figure 10: Most Of Those Who Have Had Job Impacted By Coronavirus Expect To Get Previous Employment Back Within Six Months

Less than half of those who lost their job or were laid off say their former place of employment is still open for business (39%) while most say it is closed temporarily (56%). Only 2% say their former place of employment has closed permanently.

The Uncertain Future

There are significant disagreements among policymakers and public health officials on when the U.S. economy will open back up. Some states are re-opening non-essential businesses in the coming weeks while other states warn it may be weeks or months until shelter-in-place restrictions are loosened and businesses can open up. The latest KFF Health Tracking Poll finds many Americans have already experienced an impact on either their own employment or their household income due to coronavirus. But it is also clear that many are unsure of how long this may last. About half of those who lost their job entirely say they have applied for unemployment benefits but smaller shares of those who have either been furloughed or had salary or hours reduced say the same. Additionally, only four in ten (37%) of households earning less than $40,000 annually say they have applied for these benefits. And while only 2% of those who lost their job know their businesses is closed permanently, it is unclear on what share of businesses will be able to re-open and re-hire their former employees in the next six months.

The Impacts of Coronavirus on Health Insurance Coverage, Mental Health, and Wellbeing

Changes in employment status could have serious implications for access to health insurance coverage for adults in the U.S. The latest KFF Health Tracking Poll finds that among those who lost a job or had a reduction in salary or hours (either themselves or a spouse), most report no change in their health insurance status at this point.

About eight in ten of those who say they or their spouse lost a job or were laid off (77%) or had hours or income reduced (82%) say they are still currently insured. Few (9% and 2%, respectively) say they are newly insured because they lost employer-sponsored coverage or another form of coverage. It is unclear whether those who so far have remained insured in the face of job loss will be able to retain their coverage, or whether the share losing such coverage may rise over the course of the outbreak.

Table 2: Health Insurance Coverage Among Those Who Say They Or Their Spouse/Partner Experienced A Loss Of Employment
Percent who say they are:	Self or spouse experienced any type of income or job loss (42% of total)	Self or spouse lost a job or were laid off (23% of total)	Self or spouse furloughed or had hours or income reduced (23% of total)
Currently insured	79%	77%	82%
Currently uninsured (NET)	19	22	15
Newly uninsured (lost ESI or other coverage)	6	9	2
Uninsured before loss of job or income	13	13	13

The Impact On mental health and wellbeing

Adults in the U.S. report that worry and stress related to the coronavirus outbreak is affecting their mental health and wellbeing in various ways. Four in ten say such worry or stress has led to problems with their sleep, while one-third say they either have had a poor appetite or have been over-eating. Some also say worry or stress related to the coronavirus outbreak has caused them to experience frequent headaches or stomachaches (18%), difficulty controlling their temper (15%), or increasing their alcohol or drug use (13%). About one in ten (9%) say coronavirus-related stress has led to worsening chronic health conditions. Over half of U.S. adults (56%) report that worry or stress related to the coronavirus outbreak has affected them in at least one of these ways.

Figure 11: Majority Say Worry Or Stress Related To Coronavirus Has Had Adverse Effects On Health Or Wellbeing

Majorities across most demographic groups including race and ethnicity, gender, and income report that worry or stress related to coronavirus has caused adverse effects on their mental health or wellbeing in the past two months. To see more on the implications of COVID-19 for mental health, see this KFF analysis.

Figure 12: Majorities Across Most Groups Report Worry Or Stress Related To Coronavirus Outbreak Has Impacted Mental Health

The coronavirus outbreak’s impact on mental health and wellbeing also seems to be affecting large shares of frontline health care workers and their families, as well as those who experienced a loss of employment income. About two-thirds of those living in a household with a health care worker (64%) and a similar share of those who have lost their job or income (65%) say that worry or stress related to the coronavirus outbreak has caused them to experience at least one adverse effect on their mental health or wellbeing in the past two months.

Figure 13: Coronavirus-Related Negative Health Impacts Highest Among Households With Health Care Workers Or Loss Of Employment

Since the coronavirus outbreak one in five (19%) say they have needed but been unable to get some type of medical care or prescription medications. This includes 16% who say they were unable to get medical care for conditions or concerns unrelated to coronavirus, 4% who say they were unable to get prescription medications, and 3% who say they were unable to get needed mental health services.

Figure 14: One In Five Say They Have Not Been Able To Get Medical Care, Prescriptions, Or Mental Health Services

Endnotes

¹ – “Jobless claims: Another 4.427 million Americans file for unemployment beenfits.” Yahoo! Finance (April 23, 2020). https://finance.yahoo.com/news/coronavirus-covid-weekly-initial-jobless-claims-april-18-165759755.html

²– This is consistent with the KFF Early April Health Tracking Poll that found four in ten (39%) adults reporting they had already either lost their job, lost income, or had their hours reduced without pay because of the coronavirus outbreak.

Methodology

This KFF Health Tracking Poll was designed and analyzed by public opinion researchers at the Kaiser Family Foundation (KFF). The survey was conducted April 15^th – 20th, 2020, among a nationally representative random digit dial telephone sample of 1,202 adults ages 18 and older, living in the United States, including Alaska and Hawaii (note: persons without a telephone could not be included in the random selection process). The sample included 290 respondents reached by calling back respondents that had previously completed an interview on the KFF Tracking poll at least nine months ago. Computer-assisted telephone interviews conducted by landline (261) and cell phone (941, including 661 who had no landline telephone) were carried out in English and Spanish by SSRS of Glen Mills, PA. To efficiently obtain a sample of lower-income and non-White respondents, the sample also included an oversample of prepaid (pay-as-you-go) telephone numbers (25% of the cell phone sample consisted of prepaid numbers) as well as a subsample of respondents who had previously completed Spanish language interviews on the SSRS Omnibus poll (n=10). Both the random digit dial landline and cell phone samples were provided by Marketing Systems Group (MSG). For the landline sample, respondents were selected by asking for the youngest adult male or female currently at home based on a random rotation. If no one of that gender was available, interviewers asked to speak with the youngest adult of the opposite gender. For the cell phone sample, interviews were conducted with the adult who answered the phone. KFF paid for all costs associated with the survey.

The combined landline and cell phone sample was weighted to balance the sample demographics to match estimates for the national population using data from the Census Bureau’s 2018 American Community Survey (ACS) on sex, age, education, race, Hispanic origin, and region along with data from the 2010 Census on population density. The sample was also weighted to match current patterns of telephone use using data from the July-December 2018 National Health Interview Survey. The weight takes into account the fact that respondents with both a landline and cell phone have a higher probability of selection in the combined sample and also adjusts for the household size for the landline sample, and design modifications, namely, the oversampling of prepaid cell phones and likelihood of non-response for the re-contacted sample. All statistical tests of significance account for the effect of weighting.

The margin of sampling error including the design effect for the full sample is plus or minus 3 percentage points. Numbers of respondents and margins of sampling error for key subgroups are shown in the table below. For results based on other subgroups, the margin of sampling error may be higher. Sample sizes and margins of sampling error for other subgroups are available by request. Note that sampling error is only one of many potential sources of error in this or any other public opinion poll. Kaiser Family Foundation public opinion and survey research is a charter member of the Transparency Initiative of the American Association for Public Opinion Research.

Group	N (unweighted)	M.O.S.E.
Total	1,202	± 3 percentage points

Party Identification
Democrats	348	± 6 percentage points
Republicans	341	± 6 percentage points
Independents	347	± 6 percentage points

Age
18-39	314	± 6 percentage points
40-59	384	± 6 percentage points
60-74	353	± 6 percentage points
75 and older	132	±10 percentage points

60 and older	485	±5 percentage points

Endnotes

Counties were classified by their confirmed number of coronavirus deaths as of April 17, 2020. Data for deaths attributed to coronavirus by county were taken from the Coronavirus (Covid-19) Data in the United States repository maintained by the New York Times (downloaded on April 18th). ↩︎
“Trump says states can start reopening” (April 16, 2020) The New York Times. https://www.nytimes.com/2020/04/16/us/politics/coronavirus-trump-guidelines.html ↩︎
Centers for Disease Control and Prevention, Coronavirus Disease 2019, Contact Tracing. https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/contact-tracing.html ↩︎

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More than 10,000 People in Long-Term Care Facilities Have Died Due to COVID-19

KFF Data Note Presents State-Level Cases and Deaths in Such Facilities

Published: Apr 23, 2020

More than 10,000 residents and staff in long-term care facilities across the U.S. have died from COVID-19 infections, according to a KFF analysis of state data. That number is an undercount since not all states are currently reporting such data.

Among those reporting data, the largest death tolls as of April 23 were in several Northeastern states, including New York (3,505 deaths), New Jersey (2,050), Massachusetts (1,205) and Pennsylvania (845). The data also show that there have been nearly 51,000 infections with COVID-19 at more than 4,000 long-term care facilities in the 36 states reporting such data. New Jersey reported the highest number of cases (11,608) and North Dakota the least (61).

Residents of long-term care facilities are among the most vulnerable to infection and serious illness from COVID-19, given the population density in such facilities and residents’ underlying health conditions. Moreover, nearly 40 percent of nursing homes in the U.S. had infection control deficiencies in 2017, a problem that may contribute to high numbers of cases and deaths.

Long-term care facilities account for a notable share of all COVID-19 cases and deaths in many states. In six states – Colorado, Delaware, Massachusetts, Oregon, Pennsylvania and Utah – such deaths account for over 50 percent of all COVID-19 deaths. Overall, cases in long-term care facilities make up 11 percent of all coronavirus cases in the 29 states that report cases. Deaths in long-term care facilities account for 27 percent of all deaths in the 23 states that report deaths.

“The situation in many nursing homes is an emergency. It may be time to consider sending military health response teams to nursing homes and temporarily moving nursing home residents who are able to community and rural hospitals where there is room,” said Drew Altman, KFF’s President and CEO.

Until recently, there was no federal requirement for nursing homes to report coronavirus outbreaks and COVID deaths, leading to an information gap for families, residents, and policymakers. On April 19, the Centers for Medicare and Medicaid Services released guidance that would require nursing homes to report cases of coronavirus directly to the Centers for Disease Control and Prevention (CDC). This data is not yet available.

KFF is gathering data obtainable through state COVID reporting or state press releases. We include all available long-term care facility data reported by state, including cases among both residents and staff, where available. Definition of long-term care facility differs by state, but data reflects a combination of nursing facilities, residential care communities, adult care centers, intermediate care facilities, and/or other congregate settings.

Contact

Chris Lee

clee@kff.org

State Reporting of Cases and Deaths Due to COVID-19 in Long-Term Care Facilities

Author: Priya Chidambaram

Published: Apr 23, 2020

Note: More recent figures are available in the “Additional State-level Data” section of our state COVID-19 data and policy actions tracker.

Introduction

COVID-19 has had a disproportionate effect on people who reside or work in long-term care facilities, including the 1.3 million individuals in nursing homes; 800,000 in assisted living facilities; 75,000 in intermediate care facilities; and 3 million people who work in skilled nursing or residential care facilities. CMS has released a series of guidelines targeted at reducing the impact of COVID-19 on nursing homes and other long-term care facilities, including guidance related to visitor restrictions, infection control guidelines, and designating separate facilities for coronavirus positive patients and negative patients. Until recently, however, there was no federal requirement for nursing homes to report coronavirus outbreaks and COVID-19 deaths, leading to an information gap for families, residents, and policymakers. On April 19, 2020, CMS released guidance that would require nursing homes to report cases of coronavirus directly to the Centers for Disease Control and Prevention (CDC). This data is not yet available, but there are a number of states that are already reporting data on long-term care facilities.

This data note provides data on long-term care facility cases and deaths by state (Table 1 and Table 2). We use data reported directly by states (versus through news outlets) through either state COVID-19 reporting or state press releases. The one exception to this is Texas’ counts, which are official state numbers sent directly to reporters 3x/week. We include all available long-term care facility data reported by state, including cases among both residents and staff, where available. Definition of long-term care facility differs by state, but data reflects a combination of nursing facilities, residential care communities, adult care centers, intermediate care facilities, and/or other congregate settings. In states reporting data in “congregate settings,” which could include locations such as jails and prisons, we removed reported cases and deaths in non-long-term care settings when possible.

Our data collection effort finds that in the 23 states that publicly report death data as of April 23, 2020, there have been over 10,000 reported deaths due to COVID-19 in long-term care facilities (including residents and staff), representing 27% of deaths due to COVID-19 in those states. Our data also finds that there have been over 50,000 reported cases, accounting for 11% of coronavirus cases in 29 states. In six states reporting data, deaths in long-term care facilities account for over 50% of all COVID-19 deaths (Delaware, Massachusetts, Oregon, Pennsylvania, Colorado, and Utah; Table 2). High case and death rates may be attributed to a number of factors, including high rates of testing within long-term care facilities and low rates of testing of the general public. The total cases and deaths from reporting states accounts for just over half (53%) of all cases, and 81% of all deaths. Given that not all states are reporting data yet and the continual lag in testing, the counts of cases and deaths are an undercount of the true number of cases and deaths in long-term care facilities.

What are states already reporting?

As of April 23, 2020, 36 states were reporting some level of data about coronavirus cases and deaths in long-term care facilities, with wide variation in types of data being reported, long-term care facility definitions, inclusion of residents/staff, and frequency of updates (Table 1). There is wide variation in the types of data that states are reporting. Nine states report fully comprehensive data on cases and deaths at the facility level, including Illinois and Oregon. Other states, including Michigan, Mississippi, and Pennsylvania, report data at the county level, citing privacy concerns about reporting at the facility level. Three states provide data on facilities only, with New Mexico providing a list of facilities with reported cases, and Arizona and Maine reporting a total number of facilities. There is also some variation in the types of facilities that states include in their long-term care facility counts. Five states report data on nursing homes only, while the remaining states report on long-term facilities more generally. State definitions of “long-term care facilities” can include a range of settings, including assisted living facilities, personal care homes, and intermediate care facilities. Michigan and New Mexico are the only two states to report on congregate settings more generally, without providing a breakout for long-term care facilities. States also vary in their inclusion of residents versus staff in the reporting cases and deaths. Eight states report data on residents only, 17 states report data on residents and staff together, and the remaining states do not specify who is included in their data. States also varied in how often they update the data, with the majority of states updating the data daily. Kentucky and Utah provide the data via a daily press conference, and other states report that they update the data several times a week or weekly. Seven states, including California, Illinois, and New York, did not clearly report how often their data was updated (Table 1).

What does the available data show?

Nationwide, among the states reporting data, states indicate that cases have occurred at more than 4,000 facilities, and nearly 51,000 cases and over 10,000 deaths have been reported (Table 2). The number of long-term care facilities with known cases varies widely across states, with New Jersey (438) and Pennsylvania (407) reporting the highest number of facilities and Maine (3), Iowa (12), and New Mexico (12) reporting the lowest number of facilities (Table 2). Of the 29 states that reported case data, New Jersey reported the highest number of cases in long-term care facilities, with over 11,000 cases. North Dakota was the only state to report fewer than 100 cases in their facilities. As of April 23^rd, the total number of long-term care facilities with known cases in the 32 states that report data is about 4,000 (Table 2). New York and New Jersey reported the highest number of deaths, accounting for over half of the total reported long-term care facility deaths.

Interactive 1:

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Long-term care facilities account for a notable share of cases and deaths in many states, with these facilities accounting for over half of deaths in six states (Table 2). Long-term care facility deaths as a share of total deaths in the state varies widely between states, from 8% in South Carolina to nearly 60% in Delaware (Table 2 and Interactive 1). Long-term care facility cases also account for a notable share of overall cases in states. Over one-fifth of cases in Oklahoma and Minnesota are in long-term care facilities. Illinois, South Carolina, and Tennessee have the lowest shares of cases attributable to long-term care facilities (5%).

Looking Ahead

State data reinforce the high risk that people and staff in long-term facility settings face during the coronavirus pandemic. The individuals that reside in long-term care facilities are among the most vulnerable in the US to this virus, given occupation density of these facilities and residents’ underlying poor health. Many individuals in these facilities have underlying medical conditions that put them at serious risk if they contract the virus, and some report being anxious and afraid due to the severe nature of the outbreaks. Nearly 40% of nursing homes in the country had infection control deficiencies in 2017, which may further contribute to high numbers of cases and deaths. Many residents do not have a private home or family to return to during outbreaks, making it important to consider what types of actions policymakers can take to protect these populations.

There is wide variety in how states are currently collecting and reporting data, and it is unclear when the federal data will become available, what form it will take, and how comparable it will be across states. It is unclear how and when nationwide detailed facility-level data will be made available to the public and how often the data will be updated. Data currently available from states provides an incomplete picture, but can be used to direct attention the issue and help inform policy solutions to help mitigate the number of deaths going forward.

Table 1: Variations in State Reports of Long-Term Care Facility Cases and Deaths Related to COVID-19
State (36 states)	What data is reported?	What types of facilities are included?	Does the data include residents and/or staff?	How often is data updated?
Alabama	Total cases	Long-term care facilities	Residents and Staff	Daily
Arizona	Total facilities	Long-term care facilities	Not reported	Daily
Arkansas	Total cases	Nursing homes	Residents	Daily
California	Cases by facility	Nursing homes	Residents and staff	Unspecified
Colorado	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Weekly
Connecticut	Cases by facility	Nursing homes	Residents	Unspecified
Delaware	Deaths by facility and total cases	Long-term care facilities	Residents	Daily
District of Columbia	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Daily
Florida	Deaths by county and list of facilities	Long-term care facilities	Residents and Staff	Daily
Georgia	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Daily
Illinois	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Unspecified
Iowa	Cases by facility	Long-term care facilities	Residents and Staff	Daily
Kentucky	Total cases and deaths	Long-term care facilities	Residents and Staff	Daily (press conference)
Louisiana	Total cases and deaths	Long-term care facilities	Residents and Staff	Daily
Maine	Total facilities	Long-term care facilities	Not reported	Unspecified
Massachusetts	Cases by facility and total deaths	Nursing Homes	Residents and staff	Daily
Michigan	Facility count by country	Congregate settings	Not reported	Unspecified
Minnesota	Total cases	Long-term care facilities	Residents	Daily
Mississippi	Cases and deaths by county	Long-term care facilities	Unspecified	Daily
Nevada	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Daily (on weekdays)
New Jersey	Cases and deaths by facility	Long-term care facilities	Unspecified	Daily
New Mexico	List of facilities	Congregate settings	Not reported	Daily
New York	Deaths by facility	Nursing homes	Residents	Unspecified
North Carolina	Total cases and deaths	Long-term care facilities	Unspecified	Daily
North Dakota	Cases by facility	Long-term care facilities	Unspecified	Daily
Ohio	Cases by facility	Long-term care facilities	Residents and Staff	Daily
Oklahoma	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Daily
Oregon	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Weekly
Pennsylvania	Facilities, cases, and deaths by county	Long-term care facilities	Residents and Staff	Daily
South Carolina	Total facilities, cases, and deaths	Long-term care facilities	Unspecified	2x/week
Tennessee	Cases and deaths by facility	Long-term care facilities	Residents and Staff	Unspecified
Texas	Total facilities and total deaths	Long-term care facilities	Residents	3x/week
Utah	Total facilities and total deaths	Long-term care facilities	Unspecified	Daily (press conference)
Virginia	Total facilities, cases, and deaths	Long-term care facilities	Unspecified	Daily
West Virginia	Total cases	Long-term care facilities	Residents	Daily
Wisconsin	Total cases	Long-term care facilities	Residents	Daily
NOTES: Data was collected was from available state reports and/or press releases. States that reported multiple long-term care facility settings were re-categorized as “Long-term care facilities”.

Table 2: State Reports of Long-Term Care Facility Cases and Deaths Related to COVID-19 (as of 4/23/2020)
State	Data Reporting Status	Long-term care facilities with known cases	Cases in long-term care facilities	Deaths in long-term care facilities	Long-term care facility cases as a share of total state cases	Long-term care facility deaths as a share of total state deaths
TOTAL	Reporting (36 states),Not Reporting (15 states)	4,069 (32 states)	50,775(29 states)	10,378(23 states)	11%(29 states)	27%(23 states)
Alabama	Reporting		687		12%
Alaska	Not Reporting
Arizona	Reporting	88
Arkansas	Reporting		170		7%
California	Reporting	258	3,030		8%
Colorado	Reporting	79	1,845	256	17%	50%
Connecticut	Reporting	108	1,713	375	8%	24%
Delaware	Reporting	14	237	52	7%	58%
District of Columbia	Reporting	14	199	15	6%	11%
Florida	Reporting	324	2,333	248	8%	27%
Georgia	Reporting	273	3,316	336	16%	40%
Hawaii	Not Reporting
Idaho	Not Reporting
Illinois	Reporting	189	1,850	284	5%	18%
Indiana	Not Reporting
Iowa	Reporting	12	404		11%
Kansas	Not Reporting
Kentucky	Reporting	54	650	72	19%	39%
Louisiana	Reporting	205	2,402	464	10%	32%
Maine	Reporting	3
Maryland	Not Reporting
Massachusetts	Reporting	273	7,721	1,205	18%	55%
Michigan	Reporting	243
Minnesota	Reporting	98	627		23%
Mississippi	Reporting	74	515	65	11%	34%
Missouri	Not Reporting
Montana	Not Reporting
Nebraska	Not Reporting
Nevada	Reporting	32	372	33	9%	18%
New Hampshire	Not Reporting
New Jersey	Reporting	438	11,608	2,050	12%	40%
New Mexico	Reporting	12
New York	Reporting	83		3,505		18%
North Carolina	Reporting	54	1,350	117	18%	43%
North Dakota	Reporting	14	61		9%
Ohio	Reporting	91	781		6%
Oklahoma	Reporting	42	597	63	21%	37%
Oregon	Reporting	19	293	43	14%	55%
Pennsylvania	Reporting	407	5,954	845	16%	51%
Rhode Island	Not Reporting
South Carolina	Reporting	45	240	11	5%	8%
South Dakota	Not Reporting
Tennessee	Reporting	22	375	37	5%	22%
Texas	Reporting	327		207		36%
Utah	Reporting	82		17		50%
Vermont	Not Reporting
Virginia	Reporting	92	988	78	10%	22%
Washington	Not Reporting
West Virginia	Reporting		123		13%
Wisconsin	Reporting		334		7%
Wyoming	Not Reporting
NOTES: Data was collected from available state reports and/or press releases. All available LTC facility data reported by state is presented. Number of cases is reported for residents and staff, where available. Definition of “long-term care facility” differs by state, but data reflects a combination of nursing facilities, residential care communities, adult care centers, intermediate care facilities, and/or other long-term care facilities. Given the rapidly developing situation and states reporting new data every day, updated data may be available for states at the time of publication. Total cases and deaths data can be accessed here – https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/ (Accessed April 23rd, 2020).

Donor Funding for the Global Novel Coronavirus Response

Author: Kellie Moss

Published: Apr 23, 2020

First reported at the end of December 2019, the coronavirus disease (COVID-19) pandemic shows no sign of abating, as spread continues around the world. While donors are providing support to the World Health Organization (WHO), other multilateral organizations, and directly to address needs in low- and middle-income countries, there is currently no centralized repository for this information. This tracker provides an accounting of publicly available information on donor funding to date; estimates should be treated as a floor. Not included is funding from governments for their own domestic response efforts or commitments focused on economic stimulus or recovery efforts related to the outbreak (such as an $8 billion commitment from the World Bank’s International Finance Corporation or a $100 billion commitment to its emergency rapid-disbursing capacity¹ from the International Monetary Fund).² This tracker will be updated as needed. Data are provided in the table below, including by donor, funding amount, purpose or recipient, and source. Key highlights are as follows.

As of April 21, 2020, donors (including governments, multilateral organizations, and private funders) have pledged or distributed an estimated $19.3 billion in overall financial support for the COVID-19 response. This includes donor assistance provided directly to countries as well as their contributions to the World Health Organization (WHO).
The vast majority (85%) was provided by donor governments (including the U.S.), the World Bank, and other multilateral organizations. The remainder (15%) came from non-profits, foundations, and businesses.
The largest donor to date is the World Bank³ ($6 billion), followed by the Asian Development Bank ($4.87 billion), the United States ($2.39 billion), Start Small LLC/Start Small Foundation (up to $1.21 billion), and the Global Fund to Fight AIDS, Tuberculosis and Malaria (up to $1 billion). This makes the U.S. the largest donor government.

Know about other donor funding? Contact us.

Table 1: Pledged Donor Funding for the Global Response to the Novel Coronavirus (COVID-19), Jan. – April 21, 2020
Donor	Estimated Funding(in US$ millions)	Purpose or Recipient	Data Source
TOTAL	19,292.736
*Donor Governments and Multilateral Organizations*	*16,469.695*
Asian Development Bank	4,869.100	Coronavirus relief efforts in Asia, UNICEF	Asian Development Bank, OCHA, UNICEF
Australia	4.186	WFP, UNICEF, WHO’s Pacific regional coronavirus response plan	OCHA, WHO, Australia
Austria	2.205	WHO	OCHA
Azerbaijan	5.000	WHO	WHO, OCHA
Canada	55.530	FAO, Intl. Committee of the Red Cross, Intl. Federation of Red Cross and Red Crescent Societies, IOM, PAHO, UNICEF, UNHCR, UNFPA, UNRWA, WFP, WHO	OCHA, Canada, WHO
China	20.100	WHO	WHO, China, OCHA
Czech Republic	0.258	WHO	WHO, Czech Republic, OCHA
Denmark	58.917	Adventist Development and Relief Agency, Danish People’s Aid, Intl. Committee of the Red Cross, Intl. Federation of Red Cross and Red Crescent Societies, Save the Children, UNICEF, UNHCR, UNFPA, WFP, WHO	OCHA
Estonia	0.137	Intl. Federation of Red Cross and Red Crescent Societies, WHO, China	OCHA
European Union	544.996	WHO, UNICEF, coronavirus relief activities in Africa and elsewhere	European Commission, European Commission/2, WHO, OCHA
Finland	2.195	WHO, Intl. Federation of Red Cross and Red Crescent	OCHA, WHO
France	165.026	19 priority countries for French aid in Africa, in the ocean basins (Madagascar, Comoros, Haiti) as well as in the Middle East; WHO	France, OCHA, WHO, WHO/Tedros (France)
Gavi, the Vaccine Alliance	Up to 200.000	A subset of lower-income countries to quickly respond to the pandemic	Gavi
Germany	54.973	WHO, World Relief, IOM, World Relief	Germany, WHO, OCHA
Global Fund to Fight AIDS, TB and Malaria	Up to 1,000.000	Global Fund recipients	Global Fund
Holy See	0.112	WHO	WHO, OCHA
Ireland	5.181	WHO, UNICEF	WHO, Ireland, OCHA
Italy	0.455	WHO	WHO, OCHA
Japan	138.914	WHO, UNICEF, UNHCR, UNOPS, WFP, IOM, Intl. Federation of Red Cross and Red Crescent Societies	OCHA, WHO
Kuwait	60.000	WHO	WHO, OCHA
Liechtenstein	0.423	WHO, WFP	OCHA
Lithuania	0.113	International Federation of Red Cross and Red Crescent Societies	OCHA
Luxembourg	0.392	WHO, UNICEF	OCHA, WHO
Malta	0.011	UNICEF	UNICEF
New Zealand	0.649	WHO	WHO, OCHA
Norway	7.341	WHO, WFP	OCHA, WHO, Norway
Organization of Petroleum Exporting Countries Fund*	0.500	WHO	OCHA
Qatar	5.000	WHO	WHO, OCHA
Russia	1.000	WHO	OCHA
Saudi Arabia	350.000	WHO, Gavi, international and regional health organizations and programs, coronavirus relief efforts in specific countries	Saudi Arabia, OCHA, WHO, WHO/Tedros (Saudi Arabia), WHO/Tedros (Saudia Arabia/2)
Singapore	0.500	WHO	WHO, OCHA
Slovakia	0.221	WHO	WHO, Slovakia, OCHA
Slovenia	0.068	WHO	WHO, OCHA
South Korea	5.800	WHO, UNICEF	OCHA, WHO/Tedros (South Korea)
Sweden	6.973	UNICEF, UNHCR	OCHA
Switzerland	1.999	Intl. Federation of Red Cross and Red Crescent Societies, WHO, China, North Korea	OCHA, Switzerland, WHO
U.N. Central Emergency Response Fund (CERF)*	95.000	WHO, UNICEF, UNFPA, IOM, UNHCR, UNDP, FAO, UN-Habitat, WFP	CERF, WHO, WHO release, OCHA
UNDP Multi-Partner Trust Fund*	1.498	WHO	WHO
United Kingdom	337.476	WHO, International Federation of the Red Cross and Red Crescent Societies, UNICEF, UNHCR, UNFPA, WFP, British Red Cross, other coronavirus relief efforts in vulnerable countries	United Kingdom (4/12), OCHA, WHO, United Kingdom (3/26), United Kingdom (3/6), United Kingdom (2/8)
United States	2,394.000	Prevent, prepare for, and respond to coronavirus abroad, WHO, UNICEF, China, other impacted countries (both directly and through multilateral orgs)	H.R. 748, H.R. 6074, State Dept, USAID, WHO, OCHA
U.N. Office for the Coordination of Humanitarian Affairs (OCHA)*	64.545	WHO, UNICEF, specific country responses	OCHA, WHO
WHO Contingency Fund for Emergencies (CFE)*	8.900	WHO	WHO/CFE
World Bank	6,000.000	WHO; help developing countries (prioritizing the poorest countries and those at high risk with low capacity) strengthen health systems, including better access to health services to safeguard people from the epidemic, strengthening disease surveillance, bolstering public health interventions	World Bank (3/17), World Bank, WHO, OCHA
International Development Association (IDA)	3,300.000	will provide grants and low-interest loans for low-income countries	World Bank
International Bank of Reconstruction and Development (IBRD)	up to 2,700.000	will provide loans for middle -income countries	World Bank
*Non-Profits, Foundations, and Businesses*	*2,823.040*
Adani Foundation	13.116	India/PM’s Citizen Assistance and Relief in Emergency Situations Fund (PM CARES Fund)	Gautam Adani
Alibaba	144.000	Coronavirus relief efforts in Wuhan and Hubei province, China	Axios
Align Technology, Inc.	0.143	Chinese Red Cross Foundation	U.S. Chamber Foundation
Aliko Dangote Foundation	5.200	Coronavirus relief efforts in Nigeria	Forbes
Allianz/Allianz China Insurance Holding	0.570	Coronavirus relief efforts in China	U.S. Chamber Foundation
American Express/American Express Foundation	3.753	WHO, Hubei Red Cross Foundation, Give2Asia, The Resource Foundation for relief organizations in Mexico and South America, various government relief funds at the central and state level in India	U.S. Chamber Foundation
Amgen/Amgen Foundation	Up to 12.500	Global and U.S. coronavirus relief efforts	Amgen
Anta Sports	1.446	Chinese Charity Federation	FDRA
Apple	15.000	Coronavirus relief efforts in China, globally	Apple, Reuters
Applied Materials and Applied Materials Foundation	0.574	Coronavirus relief efforts in China	U.S. Chamber Foundation
Baidu	43.377	Coronavirus relief efforts in China	Baidu
Bayer	0.660	Coronavirus relief efforts in Wuhan, China	Bayer
BD	1.100	Coronavirus relief efforts in China, U.S., and elsewhere through Direct Relief, the International Medical Corps, Americares, WHO, Project HOPE, the CDC Foundation, and the Wuhan Red Cross	BD
BHP	0.100	Coronavirus relief efforts in Mexico	BHP
Bill and Melinda Gates Foundation	190.000	WHO, coronavirus relief efforts in sub-Saharan Africa and South Asia, Chinese frontline responders, others at the global and national levels	Gates Foundation, Gates Foundation/2
Binance	1.440	Coronavirus relief efforts	U.S. Chamber Foundation
Bloomberg	40.000	WHO, coronavirus relief efforts in low- and middle-income countries,	Bloomberg press release, OCHA
Boehringer Ingelheim	0.144	Chinese Red Cross Foundation	Boehringer Ingelheim
BUA Cement	0.772	Coronavirus relief efforts in Nigeria	Forbes
ByteDance	28.918	Chinese Red Cross Foundation	China Daily
Cargill	0.288	Chinese Red Cross	U.S. Chamber Foundation
Caterpillar Foundation	8.750	WHO, China Women’s Development Foundation, other organizations and COVID-19 funds around the world, including the U.S.	Caterpillar
Chevron/Chevron Global Community Fund	0.300	China Women’s Development Foundation	U.S. Chamber Foundation
China Merchants Bank	28.918	Coronavirus Infection Pneumonia Epidemic Prevention and Control Headquarters in Wuhan	China Merchants Bank
Chubb Charitable Foundation	9.000	Organizations around the world that are providing essential resources immediately in areas facing the most acute need from the COVID-19 pandemic	Chubb
Cisco	8.000	WHO, global coronavirus response	Cisco
Citadel and Citadel Securities	7.500	Hubei Xinhua Hospital in Wuhan, China; coronavirus relief efforts in Hubei province, China, and in U.S.; assisting affected families	Citadel
Citi Foundation	10.000	WHO, international country-specific efforts in places that are severely impacted	U.S. Chamber Foundation
Clara Lionel Foundation	6.000	WHO, International Rescue Committee, Direct Relief, Feeding America, Partners in Health, Medecins Sans Frontieres	Clara Lionel Foundation, Clara Lionel Foundation/2
Clorox Company Foundation	4.000	Direct Relief, CDC Foundation	U.S. Chamber Foundation
Coca-Cola Foundation	5.350	Amref Health Africa, International Federation of Red Cross and Red Crescent Societies, Shenzhen One Foundation, U.P. Medical Foundation, UNDP country programs, various Red Cross societies, and other organizations	Coca-Cola Foundation
Conrad N. Hilton Foundation/King Baudouin Foundation United States	5.250	Coronavirus relief efforts in the African region through WHO/AFRO, UNICEF, Shining Hope for Communities (Kenya)	Hilton, Hilton/2, WHO, OCHA
Dalio Philanthropies/Bridgewater Associates LP	10.000	Peking University First Hospital, Union Hospital for Clinical Care, three medical teams led by academics in Wuhan, China	Bloomberg (2/17)
Dell, Inc.	0.288	Chinese Red Cross	U.S. Chamber Foundation
Denso Group	0.145	Red Cross Society of China	U.S. Chamber Foundation
Dow Company Foundation	2.000	WHO, Direct Relief, local and regional non-profit organizations around the globe	Dow
Education Cannot Wait Fund	0.500	Plan International	OCHA
Estée Lauder Companies and MAC Cosmetics VIVA GLAM Fund	14.200	Medecins Sans Frontieres, coronavirus relief efforts in China and other regions and countries	Estee Lauder
Federation Internationale de Football Association (FIFA)	10.000	WHO	OCHA
FUZAMEI	0.014	Coronavirus relief efforts	U.S. Chamber Foundation
Galaxy Entertainment Group Foundation	2.500	Coronavirus relief efforts	U.S. Chamber Foundation
General Motors	0.715	Chinese Red Cross Foundation	U.S. Chamber Foundation
Google.org	50.000	Coronavirus relief efforts, Hubei Red Cross Foundation	U.S. Chamber Foundation
Henderson Land Development Co.	1.400	Anti-epidemic foundation	Bloomberg (2/10)
Hewlett Packard Foundation	1.000	Direct Relief	U.S. Chamber Foundation
Hispanics in Philanthropy	0.200	Frontline organizations responding to the immediate needs of migrants and refugees to respond to the COVID-19 pandemic along the Central America, Mexico and U.S. migrant corridor	HIP
Honda Motor Company, LTD.& Honda Motor (China) Investment Co., Ltd.	1.431	Red Cross in Hubei province, China	U.S. Chamber Foundation
Huami	1.500	Coronavirus relief efforts in Wuhan, China	U.S. Chamber Foundation
iFlyTek	1.400	Coronavirus relief efforts	U.S. Chamber Foundation
Intel/Intel Foundation	5.000	International Red Cross, coronavirus relief efforts in U.S., Costa Rica, India, Ireland, Israel, Malaysia, Mexico and Vietnam	Intel
J.P. Morgan	1.000	China Foundation for Poverty Alleviation	U.S. Chamber Foundation
JSPL	3.300	India/PM CARES Fund	Forbes
JSW Group	13.200	Coronavirus relief efforts in India	Forbes
Kering	1.100	Red Cross in Hubei province, China	U.S. Chamber Foundation
Kimberly-Clark and the Kimberly-Clark Foundation	5.000	UNICEF, International Federation of Red Cross and Red Crescent Societies, American Red Cross	U.S. Chamber Foundation
Kotak Mahindra Bank	5.000	Coronavirus relief efforts in India, India/PM CARES Fund	Forbes
Kuaishou	14.459	Wuhan, China, municipal government	Kuaishou
Las Vegas Sands Corporation	2.500	Coronavirus relief efforts	U.S. Chamber Foundation
Li Ka Shing Foundation	13.000	Red Cross Society of China	Bloomberg (2/10)
Lilly China	0.150	Red Cross	U.S. Chamber Foundation
Lilly Foundation	0.250	Coronavirus relief efforts in China by Direct Relief and Project HOPE	U.S. Chamber Foundation
L’Oreal	0.720	Coronavirus relief efforts in China	U.S. Chamber Foundation
LVMH	2.300	Coronavirus relief efforts	U.S. Chamber Foundation
Lysol	2.000	CDC Foundation	U.S. Chamber Foundation
Mastercard	0.300	China Development Fund	U.S. Chamber Foundation
Medtronic/Medtronic Foundation	1.500	Coronavirus relief efforts	U.S. Chamber Foundation
Meituan Public Welfare Foundation	28.918	Coronavirus relief efforts in China	Meituan-Dianping
Melco Resorts & Entertainment	2.600	Coronavirus relief efforts	U.S. Chamber Foundation
Merck	1.120	WHO, CDC Foundation, Direct Relief, Project HOPE, Given2Asia, Chinese Red Cross Foundation	U.S. Chamber Foundation
MGM Resorts International/MGM China Holdings	2.500	Coronavirus relief efforts	U.S. Chamber Foundation
Michael and Susan Dell Foundation	80.000	Coronavirus relief efforts across the world, other efforts	Dell Foundation
Micron Foundation	0.143	Give2Asia for support of frontline health workers and hospitals	U.S. Chamber Foundation
Microsoft	0.144	Coronavirus relief efforts in Wuhan and Hubei province, China	U.S. Chamber Foundation
Midea Group	14.375	Red Cross Society of Hubei provision, China	U.S. Chamber Foundation
Mike Adenuga Foundation	4.095	Coronavirus relief efforts in Nigeria	Guardian Nigeria
Mondelez International/Mondelez International Foundation	15.000	Coronavirus relief efforts in China and other countries, including the U.S.	U.S. Chamber Foundation, Mondelez
Moody’s Corporation	0.600	WHO, Direct Relief, Medecins Sans Frontiers, Save the Children, Give2Asia, coronavirus relief efforts in U.S.	U.S. Chamber Foundation
Morgan Stanley	8.500	WHO, CDC Foundation, coronavirus relief efforts in Wuhan, China, and elsewhere	Morgan Stanley
Muyuan Group/Qin Yinglin and Qian Ying	28.918	Coronavirus relief efforts in China	Muyuan Group
New World Development Group	7.000	Coronavirus relief efforts in Greater China	Forbes
New York Life	0.450	Center for Disaster Philanthropy’s Covid-19 Response Fund, CDC Foundation	U.S. Chamber Foundation
Nike	3.100	WHO, China Youth Development Foundation, community partners across Europe, Middle East and Africa	U.S. Chamber Foundation
PepsiCo Foundation	17.000	Coronavirus relief efforts in Latin America, Africa, the Middle East, South Asia, Asia Pacific, Australia, New Zealand, and China	U.S. Chamber Foundation
Pfizer Foundation	0.500	Coronavirus relief efforts in China by Direct Relief and Project HOPE	U.S. Chamber Foundation
Poul Due Jensen/Grundfos Foundation	4.145	Emergency relief funding, including for WASH efforts	U.S. Chamber Foundation
Procter & Gamble	1.144	Direct Relief, coronavirus relief efforts in China	Procter & Gamble
Prudential Financial	1.500	UNICEF, Fosun Foundation (China)	Prudential
PUBG	0.430	Chinese Red Cross	Korea Herald
Qualcomm	1.000	WHO, Chinese Red Cross	U.S. Chamber Foundation
Ralph Lauren Corporate Foundation	10.000	WHO, Council of Fashion Designers of America (CFDA) / Vogue Fashion Fund for COVID-19 relief, support for people with cancer, support for employees	U.S. Chamber Foundation
Reliance Foundation	66.700	Coronavirus relief efforts in India	Reliance Foundation, Business Insider
S&P Global Foundation	2.000	UNICEF, Direct Relief, Give2Asia, other nongovernmental organizations (NGOs) focused on supporting food scarcity, medical supplies and broad COVID-19 response	U.S. Chamber Foundation
Salesforce	3.000	CDC Foundation, San Francisco’s Give2SF Fund, UCSF’s COVID-19 Response Fund	U.S. Chamber Foundation
Sawiris Foundation for Social Development	6.400	Coronavirus relief efforts in Egypt	Forbes
SC Johnson	1.143	CDC Foundation, Chinese Red Cross	U.S. Chamber Foundation
Semiconductor Manufacturing International Corporation (SMIC)	1.429	Shanghai Charity Foundation, coronavirus relief efforts in China	SMIC
Shimao Property Holdings Ltd	3.900	Coronavirus relief efforts	Bloomberg (2/10)
Shiseido	1.578	Shanghai Charity Foundation, Charity Federation of Wuhan	Shiseido
SJM Holdings Ltd	2.500	Coronavirus relief efforts in Hubei province, China	SJM Holdings
Smilegate Group	1.430	Chinese authorities to combat the coronavirus	Smilegate Foundation
Sony	10.000	WHO, Medecins Sans Frontieres, UNICEF, UNHCR	U.S. Chamber Foundation
Stanley Black & Decker	4.000	COVID-19 focused nonprofit organizations	U.S. Chamber Foundation
Starbucks	0.432	Chinese Red Cross Foundation	U.S. Chamber Foundation
Start Small LLC/Start Small Foundation ^	Up to 1,209.441	Global COVID-19 relief	Jack Dorsey
Suncity Group	3.800	Coronavirus relief efforts	U.S. Chamber Foundation
Swarovski	0.434	Coronavirus relief efforts	U.S. Chamber Foundation
TE Connectivity/TE Foundation	1.175	Direct Relief, Americares, Shanghai Charity Federation, identification and production of treatments for COVID-19	U.S. Chamber Foundation
Tencent Charity Foundation	214.715	Fund for coronavirus relief efforts in Hubei province, China, and other affected areas, Tsinghua University Education Foundation, Global Health Drug Discovery Institute	Tencent
Texas Instruments	0.300	China Youth Development Foundation	U.S. Chamber Foundation
The Hartford	1.000	CDC Foundation, Center for Disaster Philanthropy’s COVID-19 Response Fund, other coronavirus relief efforts	The Hartford
Tiffany & Co.	0.750	WHO	U.S. Chamber Foundation
TikTok	150.000	WHO, CDC Foundation, global coronavirus relief efforts	TikTok
Twilio	0.500	WHO	U.S. Chamber Foundation
UBS	1.000	“Frontline responders” in China	U.S. Chamber Foundation
UNICEF National Committee/Canada	0.345	UNICEF	OCHA
UNICEF National Committee/Denmark	1.449	UNICEF	OCHA
UNICEF National Committee/Sweden	0.211	UNICEF	OCHA
Unilever	50.000	Global hygiene campaign to help stop the spread of the coronavirus	U.S. Chamber Foundation
U.S. Fund for UNICEF	0.366	UNICEF	OCHA
Vedanta Group	26.500	Coronavirus relief efforts in India, India/PM CARES Fund	Forbes
Varian Medical Systems	1.000	Coronavirus prevention and relief efforts	U.S. Chamber Foundation
Verizon	7.000	Center for Disaster Philanthropy’s COVID-19 Response Fund, Direct Relief, WHO	Verizon, Verizon/2
Versace	0.143	Chinese Red Cross Foundation	U.S. Chamber Foundation
Visa	10.000	Coronavirus relief efforts	U.S. Chamber Foundation
Vital Strategies/Resolve to Save Lives	0.750	WHO for readiness scale-up for COVID-19 in African region and selected countries	OCHA, WHO
Walmart/Walmart Foundation	5.000	WHO	Walmart
WeMade	0.143	Hubei Charity Federation	Korea Herald
Wilbur-Ellis	0.100	Red Cross, directed to U.S., Canada, and Asia-Pacific	U.S. Chamber Foundation
Wynn Macau	2.500	Coronavirus relief efforts	U.S. Chamber Foundation
Xilinx	1.100	WHO, UCSF’s COVID-19 Response Fund, other efforts	U.S. Chamber Foundation
Xylem Watermark	3.000	Coronavirus relief efforts	U.S. Chamber Foundation
Zhejiang Geely Holding Group/Li Shufu Public Welfare Foundation	28.380	Coronavirus relief efforts in Hubei, Guangdong, Zhejiang and Henan provinces, as well as other areas around the China	Zhejiang Geely Holding Group
NOTES: Amounts may not sum to subtotals/total due to rounding. * Multiple countries contribute to these pooled funding mechanisms. U.N. is United Nations. WHO is World Health Organization. UNICEF is United Nations Children’s Fund. WHO has issued a strategic response plan that enumerates resource needs for global COVID-19 response activities from February through April 2020, focusing largely on those of low- and middle-income countries, and OCHA has issued a global humanitarian response plan that enumerates resource needs for the U.N. coordinated effort to address the needs provided by the COVID-19 pandemic for the most vulnerable. WHO is also reporting funding it has received specifically for WHO activities. A small portion of funding may be donated to U.S. relief, but where included, the amount could not be disaggregated. ^ The value of this funding fluctuates on a daily basis; this was the value on April 22, 2020. Funding in this table is attributed: to donor governments for novel coronavirus when designated for this purpose, whether provided bilaterally to China or another country, non-profit organizations, or others, or provided multilaterally through a multilateral organization or instrument; to a multilateral organization where that multilateral organization specifically designated general funds for this purpose; to other organizations, such as non-profit organizations, foundations, and businesses, when designated for this purpose. We did not include funding spent by countries on domestic responses to novel coronavirus, in-kind contributions, matching campaigns, or funding predominantly focused on research. The estimate is a floor, as funding from some donors, particularly private sector companies, is not easily identifiable.

Includes funds to its Rapid Credit Facility that provides zero-interest loans for low-income countries. ↩︎
If additional details become available that show any of this funding will be provided on concessional terms for the health response, this data note and funding totals within it will be updated. ↩︎
Specifically, its International Development Association (IDA) and International Bank for Reconstruction and Development (IBRD). ↩︎

A Look at Online Platforms for Contraceptive and STI Services during the COVID-19 Pandemic

Authors: Brittni Frederiksen, Ivette Gomez, and Alina Salganicoff

Published: Apr 23, 2020

Introduction

As the nation grapples with the onslaught of COVID-19 cases, basic needs for contraceptive services and supplies have not diminished. Most family planning clinics and health centers remain open because they are considered essential health providers, but some have had to scale back staff or hours. Many clinics are looking to telemedicine services to find ways to ensure patients continue to have access to contraception and STI services. There has been a proliferation of online services in recent years that seek to offer individuals that cannot make it to a clinic or do not have an established source of care access to some contraceptive methods and STI testing through a telemedicine platform, usually a website or a smartphone application. This data note provides an overview of options available for the online management of sexual and reproductive health services during the current global pandemic and discusses the opportunities and limitations of these platforms in terms of availability and cost.

Family Planning Clinics

Providers and clinicians are still the front lines for provision of family planning services for most individuals. The majority of clinics remain open as essential health care providers and are still serving patients for family planning and STI services. Many clinics are now using telehealth services or are quickly working to implement telehealth as a way to continue providing contraceptive care without the need for an office visit. Individuals who are able can still contact their usual provider for contraceptive refills and receive their prescriptions through their provider or a local pharmacy, which may have home delivery or drive-through pick up options. When possible, clinics are providing a 12-month supply of contraceptive methods, prescribing Depo-SubQ Provera 104 for self-injection of Depo shots which can be administered at home, and using quick start protocols for all methods to begin contraception on the day of the visit rather than wait until the next period to initiate a new method, removing the need for subsequent visits. These services are still available without cost sharing for those covered by Medicaid or private insurance, and on a sliding fee scale for low-income and uninsured people seeking care at Title X-supported family planning clinics, Planned Parenthood clinics, and Federally Qualified Health Centers.

Because of staffing shortages or concerns about infection, however, some clinics and providers have been forced to reduce hours and limit in-person visits to patients with urgent needs. Some sites may also limit services that require specific staff (with specialized training) or personal protective equipment, such as long-acting reversible contraceptive (LARC) insertions and removals.

Online Platforms for Contraception

For individuals who cannot leave their home or get their method through their usual provider or pharmacy, an increasing number of online telemedicine platforms are available for obtaining hormonal contraceptives (the pill, patch, ring, and emergency contraception) outside of traditional health care settings. Most online platforms determine a client’s eligibility and prescribe contraception in a similar manner to a clinic. Using an online questionnaire or video consultation, the provider determines a patient’s medical eligibility for contraception by obtaining their health history, including a recent blood pressure reading, and evaluating potential risk factors including history of stroke, liver or gallbladder problems, a recent pregnancy, current breastfeeding and smoking status. After assessing the patient’s medical history and contraceptive preferences, the patient is either mailed their contraceptives or their prescription is sent to a nearby pharmacy for pick-up. Most platforms will send one to three packs of pills at a time, with a few offering six to twelve packs per delivery. In addition to the cost of the contraception, many online platforms charge a consultation fee or yearly membership that ranges from $15 to $99 and typically covers the medical evaluation and customer support for the duration of the prescription. This fee is not usually billed to or covered by insurance and is instead paid out-of-pocket by the patient.

Some companies can only prescribe to individuals in certain states, though they may be able to mail contraceptives to people who already have a prescription. For instance, Pandia Health will deliver to all 50 states, but can only currently prescribe in California, Florida, and Texas; while The Pill Club can prescribe in 37 states and DC, and delivers to all 50 states. Many of these platforms require individuals to be at least 18 years old, but some provide services to younger people with parental consent.

There is considerable variation in contraceptive costs, method availability, and Medicaid and private insurance acceptance by vendor (Table 1). The price of contraception offered by these platforms varies by method and by brand. While some online platforms accept private insurance, the majority do not accept Medicaid, which may limit low-income women’s access to these services. While most offer low-cost generic contraceptives, the cost of contraceptives on platforms that do not accept insurance can range from $10 to $225 a month, which can be unaffordable for many individuals.

It is important to note that these services and methods are covered at no cost for people covered by Medicaid or most private insurance or on a sliding fee scale at a Title X clinic, Planned Parenthood, or Federally Qualified Health Center for uninsured people. Emergency contraception is available over the counter, but is also available through many of these platforms for a similar price ($20-$45). AfterPill is an online platform that specializes in emergency contraception and charges $25/pill or $65 for three pills. For faster shipping, AfterPill can be purchased through Amazon Prime.

Online Platforms for STI Testing

In addition to contraception, some of these platforms provide at-home sexually transmitted infection (STI) testing. Some platforms require a virtual consultation and charge a fee, but most allow the individual to purchase an STI test or testing kit with multiple STI tests without a consultation. STI testing kits have options for genital, oral, and rectal chlamydia and gonorrhea testing, as well as syphilis, HIV, and trichomoniasis testing. Depending on the type of kit, individuals collect either urine samples, finger prick blood samples, or vaginal, rectal, or oral swabs, and mail them back for testing. Rather than providing at-home test kits, some entities, like Lemonaid and STDcheck.com, will order lab tests and individuals can get tested at a local clinic or lab.

If the test results are positive, a medical provider will either contact the individual or the individual can schedule a telemedicine physician consultation to discuss treatment options. Many will prescribe treatment for chlamydia, gonorrhea, trichomoniasis, and herpes simplex virus 2 that can be picked up at a local pharmacy. The physician consultation is usually provided at no additional cost, but the cost of the treatment depends on an individual’s insurance. STDcheck.com charges $95 for a physician consultation and written prescription for treatment. They will also provide a prescription for an individual’s partner for an additional $95. Lemonaid also charges a separate visit fee to assess the individual for treatment that is not included in the price of the test. Most online platforms offering STI testing do not state whether they offer expedited partner therapy (EPT). EPT is when a provider prescribes treatment for the sex partner(s) of the diagnosed patient, without first examining the partner(s). EPT is typically used in cases of chlamydia or gonorrhea, and sometimes trichomoniasis, in order to prevent reinfection by the same sex partner(s). EPT helps ensure a patient’s partner receives treatment and is a best practice in clinical settings, but many of these online platforms require an individual’s partner to set up their own account and purchase their own testing kit, which is not EPT.

These at-home tests and test kits can range in cost from $39 to $522 (Table 1). Many platforms do not accept private insurance or Medicaid, which can make this service cost prohibitive for many people. Nurx is the only platform offering at-home STI testing that accepts Medicaid, but only in California, Illinois, and Texas. The cost of their STI testing kits ranges from $75 for individuals with insurance coverage to $150 to $220 for individuals who are uninsured. Alpha Medical, PlushCare, and Virtuwell also accept private insurance, but not Medicaid. Alpha Medical, PlushCare, and Virtuwell all require a consultation fee and provide online primary care, offering more than just at-home STI testing. Usually, the most affordable way to get STI testing and treatment is to use private insurance or Medicaid at a clinic and under the ACA some STI screenings are covered without cost-sharing. Low-income and uninsured individuals can also go to a Title X clinic, a Federally Qualified Health Center, or a public health department for no-cost or low-cost testing based on a sliding fee scale, but this may be challenging when services are scaled back and travel options limited.

Opportunities and Limitations for Online Platforms

While these online platforms may offer convenience and the ability to stay home, there are tradeoffs to consider. One significant limitation is affordability. The majority of online platforms do not accept insurance or Medicaid and do not offer sliding fee scale options for uninsured individuals. In 2018, 59% of women of reproductive age (15-49) had employer-sponsored insurance coverage, 19% had Medicaid coverage, and 12% were uninsured. Most women with insurance coverage can access contraceptives at low to no cost. The Affordable Care Act requires most private health insurance plans to cover at least one form of each of the 18 FDA-approved contraceptive methods for women as prescribed without cost-sharing, and Medicaid programs are also required to cover family planning services with no cost sharing. The relatively high cost of some contraceptives available on online platforms can put them out of reach for many low-income women. In 2018, 21% of low-income women were uninsured, 42% had Medicaid coverage, and 28% had employer-sponsored coverage.

While most hormonal contraceptive methods are available through these online platforms, some of the longer-acting, most effective methods such as IUDs and contraceptive implants still require a visit to a clinic for insertion. The same is true for most DMPA contraceptive injections. Only Nurx offers the Depo-SubQ Provera 104 injection, which is developed for self-administration, at $285/shot. Other limitations include limited availability of certain brands, age restrictions for teens, and the need for a credit or debit card for payment.

While these platforms may be an attractive option for some to secure prescription contraceptive methods and STI services during the COVID-19 outbreak without leaving home, family planning providers are ramping up their telemedicine practices to make these methods available and affordable to their teen and adult clients. Unless individuals find a platform that accepts their insurance or Medicaid, they can expect to spend considerably more for contraceptive services and STI treatment than they would have if they sought care from a provider that accepts private insurance or Medicaid or participates in a family planning clinic or health center that receives federal funding to provide care. While these online platforms offer great potential to expand access to prescription contraceptive methods and STI testing, cost will continue to be a barrier for consumers until the services broaden their participation in Medicaid and private insurance so that women can receive no-cost coverage just like they do when they obtain services and supplies in clinical settings.

News Release

Online Contraceptive and STI Services During COVID-19: What are the options?

Published: Apr 23, 2020

A new KFF issue brief provides an overview of online options for contraceptive and STI services during the COVID-19 pandemic. This resource also includes an interactive table that shows which platforms are available in each state and which accept private insurance or Medicaid.

The majority of traditional family planning clinics are still open for in-person care or are moving to telehealth services – but some clinics and providers have reduced hours and limited in-person visits to patients with urgent needs only. There are dozens of online platforms for contraception and STI management, which offer an alternative for obtaining care as clinics face staffing shortages and take measures to scale back in-person visits to reduce the risk of coronavirus transmission.

While they may offer access to contraception through a phone app or online platform, there are significant limitations – especially when it comes to affordability and coverage. Many online platforms do not accept insurance or Medicaid and do not offer sliding fee scale options for the uninsured. Unless individuals can find a platform that accepts their insurance or Medicaid, they can expect to pay considerably more for contraceptive services and STI treatment than they would for care obtained in clinical settings, which is typically covered without cost-sharing.

Contact

Nikki Lanshaw

nlanshaw@kff.org

How Many Adults Are at Risk of Serious Illness If Infected with Coronavirus? Updated Data

Authors: Wyatt Koma, Tricia Neuman, Gary Claxton, Matthew Rae, Jennifer Kates, and Josh Michaud

Published: Apr 23, 2020

View and download a PDF of this Data Note.

As the number of people in the U.S. with coronavirus disease (COVID-19) continues to grow, there is increasing concern for adults who have a higher risk of developing serious illness if they are infected. The majority of people who become infected are expected to be asymptomatic or recover without needing special treatment, according to the World Health Organization. However, based on the most current information made available by the Centers for Disease Control and Prevention (CDC), older people and younger adults with serious medical conditions, such as heart disease, diabetes, lung disease, asthma and obesity have a greater risk of becoming severely ill if they get infected with coronavirus. CDC has issued specific guidance for people who fall into these categories.

To inform discussions about the challenges associated with coronavirus in the U.S., we first analyzed data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) to estimate the total number of adults nationwide, and by state, who have an elevated risk of serious illness if they are infected with coronavirus. CDC’s understanding of risk factors continues to evolve as the disease spreads, and more is learned about the effects of the coronavirus on different populations. We have therefore updated our previously published analysis to reflect a revised definition from the CDC of adults who are at higher risk of serious illness if they get infected with coronavirus. Our updated definition of high risk now includes: older adults (ages 65 or older, rather than 60 and older) and adults between the ages of 18 and 64 with heart disease, chronic obstructive pulmonary disease (COPD), uncontrolled asthma, diabetes, or a BMI greater than 40. To avoid overestimating the number and share of adults at higher risk of serious illness, our revised analysis does not include people with cancer; this is because BRFSS asks if respondents have ever had cancer, whereas CDC at-risk guidelines are limited to people who are currently immunocompromised, including cancer patients in treatment. We have also updated our weighting methodology to provide more robust state-level estimates of the number of adults at higher risk. See Methods for more information.

Key Findings

About four in ten adults (37.6%) ages 18 and older in the U.S. (92.6 million people) have a higher risk of developing serious illness if they become infected with coronavirus, due to their older age (65 and older) or health condition (Figure 1; Table 1).
Just over half of those at higher risk of developing a serious illness are ages 65 and older (55.2% or 51.1 million adults); however, the remaining 41.4 million adults ages 18-64 are at risk due to an underlying medical condition.
The share of adults ages 18 and older who have a higher risk of developing a more serious illness varies across the country, ranging from 49.3 percent (West Virginia) to 30 percent (Utah).
In some of the states with the highest number of reported coronavirus cases thus far, the share of adults at high risk of serious illness if infected is relatively high: Louisiana and Florida (at 42.1 percent, each) and Michigan (41.2 percent).
An estimated 5.1 million adults who are at higher risk of getting a serious illness if they become infected with coronavirus are uninsured.

Figure 1: Over 90 million of 246 million U.S. Adults are at Higher Risk of Serious Illness if Infected with Coronavirus

–

Discussion

The majority of people who become infected with coronavirus are not expected to become seriously ill, but a large segment of the U.S. adult population – one third (37.6 percent) of adults ages 18 and older – have a higher risk of serious illness if they do become infected due to their age or underlying medical condition. One group particularly at risk are the 1.3 million people living in nursing homes in the US. As the number of people who test positive for coronavirus continues to rise, and as more is learned about the progression of illness and treatment among those who become seriously ill, the current set of risk factors available to estimate the size of the at-risk population is likely to be refined. Given the paucity of data at this stage of the pandemic, and the high stakes involved for people who do get seriously ill, these estimates confirm the need to take unprecedented efforts to minimize the spread of the coronavirus.

Methods

This brief analyzes data from the nationally-representative, cross-sectional 2018 Behavioral Risk Factor Surveillance System (BRFSS) of adults ages 18 and older living in the community. BRFSS is an ongoing, state-based, random-digit-dialed telephone survey of non-institutionalized civilian adults. The 2018 survey has over 430,000 respondents. Information about the BRFSS is available at http://www.cdc.gov/brfss/index.html. For this updated analysis, we calculated the number of people at risk of serious illness if they get infected with coronavirus, using BRFSS, and we controlled these estimates to the 2018 American Community Survey (ACS) to generate population counts by state and age. Because of its large sample size, the ACS is generally seen as providing reliable population estimates by state. This updated analysis reflects this change in weighting methodology. The weighted total population is about 3% less in the ACS than in BRFFSS.

The estimate of the percentage of adults at higher risk utilizes the BRFSS survey weights to account for the complex sampling design. Data exclude adults living in Guam or Puerto Rico. Data represent adults who report ever being told by a doctor that they have one of the listed conditions. To avoid overestimating the number and share of adults at higher risk of serious illness, our revised analysis does not include people with cancer; this is because BRFSS asks if respondents have ever had cancer, whereas CDC at-risk guidelines are limited to people who are currently immunocompromised, including cancer patients in treatment. Because the CDC guidelines suggest that those with moderate or severe asthma are at greater risk than those with mild asthma, we adjusted the overall total to account for the share with uncontrolled asthma, adjusting the overall total by 62 percent, based on CDC prevalence. The change in estimates from our previous analysis are mainly attributable to the revised CDC at-risk guidelines.

Table 1: Number of U.S. Adults At Higher Risk Of Serious Illness If Infected With Coronavirus
	Total, adults ages 18 and older			Adults under age 65.			Adults age 65 and older
State	Total number, adults 18 and older	Number at risk, adults 18 and older	At-risk adults, as a share of all adults ages 18 and older	Total number, adults under age 65	Number at risk due to health conditions, adults under age 65	Share of adults under age 65 at risk	Total number, adults age65 and older	Older adults, as a share of all at-risk adults
Overall	246,402,480	92,560,223	37.60%	195,281,293	41,439,036	21.20%	51,121,187	55.20%
Alaska	527,792	172,913	32.80%	442,335	87,456	19.80%	85,457	49.40%
Alabama	3,684,158	1,588,905	43.10%	2,873,170	777,917	27.10%	810,988	51.00%
Arkansas	2,231,289	971,477	43.50%	1,742,217	482,405	27.70%	489,072	50.30%
Arizona	5,389,431	2,107,495	39.10%	4,143,554	861,618	20.80%	1,245,877	59.10%
California	29,879,585	9,952,647	33.30%	24,302,948	4,376,010	18.00%	5,576,637	56.00%
Colorado	4,301,492	1,345,398	31.30%	3,506,700	550,606	15.70%	794,792	59.10%
Connecticut	2,739,381	985,861	36.00%	2,148,446	394,926	18.40%	590,935	59.90%
District Of Columbia	542,174	172,398	31.80%	459,618	89,842	19.50%	82,556	47.90%
Delaware	739,951	305,395	41.30%	562,058	127,502	22.70%	177,893	58.30%
Florida	16,683,102	7,018,513	42.10%	12,389,357	2,724,768	22.00%	4,293,745	61.20%
Georgia	7,748,091	2,803,400	36.20%	6,321,701	1,377,010	21.80%	1,426,390	50.90%
Hawaii	1,056,723	412,878	39.10%	798,667	154,822	19.40%	258,056	62.50%
Iowa	2,336,881	862,351	36.90%	1,819,010	344,480	18.90%	517,871	60.10%
Idaho	1,282,329	464,522	36.20%	1,006,426	188,619	18.70%	275,903	59.40%
Illinois	9,623,149	3,482,300	36.20%	7,697,443	1,556,594	20.20%	1,925,706	55.30%
Indiana	4,955,934	1,976,552	39.90%	3,940,531	961,149	24.40%	1,015,403	51.40%
Kansas	2,124,795	807,193	38.00%	1,683,235	365,633	21.70%	441,560	54.70%
Kentucky	3,341,735	1,455,749	43.60%	2,630,671	744,685	28.30%	711,064	48.80%
Louisiana	3,435,791	1,445,420	42.10%	2,736,213	745,842	27.30%	699,578	48.40%
Massachusetts	5,318,415	1,837,581	34.60%	4,215,360	734,526	17.40%	1,103,055	60.00%
Maryland	4,556,875	1,690,631	37.10%	3,650,537	784,293	21.50%	906,338	53.60%
Maine	1,060,936	451,409	42.50%	792,994	183,467	23.10%	267,942	59.40%
Michigan	7,649,977	3,152,031	41.20%	5,973,998	1,476,052	24.70%	1,675,979	53.20%
Minnesota	4,211,344	1,428,307	33.90%	3,354,163	571,126	17.00%	857,181	60.00%
Missouri	4,590,784	1,860,608	40.50%	3,590,211	860,035	24.00%	1,000,573	53.80%
Mississippi	2,185,597	929,387	42.50%	1,724,625	468,415	27.20%	460,972	49.60%
Montana	813,505	317,546	39.00%	615,520	119,561	19.40%	197,985	62.30%
North Carolina	7,785,717	3,038,856	39.00%	6,137,620	1,390,759	22.70%	1,648,097	54.20%
North Dakota	560,523	194,056	34.60%	450,954	84,487	18.70%	109,569	56.50%
Nebraska	1,404,355	514,118	36.60%	1,112,641	222,404	20.00%	291,714	56.70%
New Hampshire	1,059,120	428,938	40.50%	821,964	191,782	23.30%	237,156	55.30%
New Jersey	6,799,427	2,354,252	34.60%	5,396,351	951,176	17.60%	1,403,076	59.60%
New Mexico	1,575,314	619,893	39.40%	1,211,046	255,625	21.10%	364,268	58.80%
Nevada	2,307,514	833,620	36.10%	1,833,570	359,676	19.60%	473,944	56.90%
New York	15,028,787	5,550,169	36.90%	11,914,579	2,435,961	20.40%	3,114,208	56.10%
Ohio	8,817,672	3,506,796	39.80%	6,891,542	1,580,666	22.90%	1,926,130	54.90%
Oklahoma	2,879,096	1,175,713	40.80%	2,280,666	577,283	25.30%	598,430	50.90%
Oregon	3,257,427	1,297,341	39.80%	2,528,112	568,026	22.50%	729,315	56.20%
Pennsylvania	9,798,951	3,898,304	39.80%	7,547,656	1,647,009	21.80%	2,251,295	57.80%
Rhode Island	814,843	312,092	38.30%	639,889	137,138	21.40%	174,954	56.10%
South Carolina	3,838,837	1,590,048	41.40%	2,957,890	709,101	24.00%	880,947	55.40%
South Dakota	639,915	226,086	35.30%	500,965	87,136	17.40%	138,950	61.50%
Tennessee	5,105,824	2,121,941	41.60%	4,031,127	1,047,244	26.00%	1,074,697	50.60%
Texas	20,715,876	7,199,553	34.80%	17,205,161	3,688,838	21.40%	3,510,715	48.80%
Utah	2,189,374	657,278	30.00%	1,846,133	314,037	17.00%	343,241	52.20%
Virginia	6,343,962	2,275,390	35.90%	5,048,192	979,620	19.40%	1,295,770	56.90%
Vermont	490,326	191,781	39.10%	371,176	72,631	19.60%	119,150	62.10%
Washington	5,733,155	2,013,681	35.10%	4,590,384	870,910	19.00%	1,142,771	56.80%
Wisconsin	4,415,638	1,612,619	36.50%	3,459,881	656,862	19.00%	955,757	59.30%
West Virginia	1,397,752	689,770	49.30%	1,045,095	337,113	32.30%	352,657	51.10%
Wyoming	431,859	157,117	36.40%	336,991	62,249	18.50%	94,868	60.40%
NOTE: Data includes adults ages 18 and older; excludes adults living in nursing homes and other institutional settings.SOURCE: KFF analysis of 2018 Behavioral Risk Factor Surveillance System.

News Release

Interactive Maps Highlight Urban-Rural Differences in Hospital Bed Capacity

Rural Areas Have Fewer ICU Beds and Populations at Greater Risk for COVID-19 Complications

Published: Apr 23, 2020

As the U.S. coronavirus outbreak spreads beyond densely populated metropolitan areas, a new KFF analysis finds that rural areas typically have fewer intensive care hospital resources than their urban counterparts, and populations at greater risk of developing serious illness and complications from COVID-19.

While metro and non-metro areas have similar numbers of hospital beds per capita (23.5 vs 23.8 beds per 10,000 people), non-metro areas have fewer intensive care (ICU) beds – about 1.7 per 10,000 people, compared to 2.8 in urban areas. When adjusted for age, non-metro areas have only 1.6 ICU beds per 10,000 age-adjusted population, compared to 2.9 ICU beds in metro areas. Since older adults with COVID-19 are both more likely to require hospitalization and more likely to require intensive care while hospitalized, outbreaks in rural communities could strain the already limited capacity of their health systems.

The analysis includes interactive maps that enable users to explore estimates of hospital bed capacity by area, and to adjust those estimates based on the age of the population.

On average, residents of rural areas tend to be older and sicker than people who live in urban areas. 20% of people living in non-metro areas are age 65 or older, and 26% of residents under age 65 have health conditions that put them at a higher risk of developing serious COVID-19 complications. Many may already face challenges accessing health care services due to factors like provider shortages and longer travel times to reach hospitals.

The analysis is part of the Peterson-KFF Health System Tracker, an online information hub dedicated to monitoring and assessing the performance of the U.S. health system. For more data, analysis, polling and journalism on the COVID-19 pandemic, visit our special resource page on kff.org.

Contact

Ashley Joyce

KFF

ashleyj@kff.org

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