Introduction

Women’s access to health care is shaped by a broad range of factors, including coverage and income, the availability of health care providers in their communities, and the affordability of care and health insurance.

The Affordable Care Act (ACA) has expanded pathways to affordable coverage to millions of women by broadening eligibility for Medicaid and providing subsidies to make individual health insurance coverage more affordable for those who do not have access to coverage through their employer. The ACA also contains provisions aimed at alleviating some of the financial barriers to health care; however, many women still face challenges with health care costs and medical bills, particularly those who are uninsured or have low incomes.

This report presents findings from the 2022 KFF Women’s Health Survey (WHS) on women’s health status, use of health care services, and costs. The WHS is a nationally representative survey of 5,145 self-identified women ages 18 to 64, conducted May 10 – June 7, 2022. See the Methodology for more details.

Summary of Findings

• The majority of women ages 18-64 report being in excellent, very good, or good health (82%); however, nearly one in five (18%) women describe their health as fair or poor, with substantial differences by sociodemographic characteristics.
• Half of women (49%) report having an ongoing health condition that requires regular monitoring, medical care, or medication and 18% report having a disability or chronic disease that keeps them from participating fully in work, school, housework, or other activities, with higher rates among older women. Ten percent of women with a disability or ongoing health condition do not have a regular doctor or health care provider.
• Nearly all women ages 18-64 (95%) have seen a doctor or health care provider in the past two years, but a smaller share has had a general check-up or well-woman visit (73%). Uninsured women are significantly less likely than insured women to have seen a provider or had a check-up in the past two years.
• Overall, women have more interactions with and connections to the health care system than men. Higher shares of women report having a usual source of care and have visited a health care provider in the past two years than men.
• Most women (79%) obtain their health care at doctors’ offices, but health centers and clinics are common sites of care for underserved populations. One in four (25%) uninsured women, one in five (19%) Hispanic women, and 17% of those with Medicaid usually visit health centers or clinics for their routine health care.
• Most women ages 18-64 (60%) say they have had a telemedicine visit in the past two years. Most (69%) rate the quality of care they received via telemedicine as comparable to in-person care. Annual check-ups, minor illnesses and injuries, and mental health services were the top reasons women accessed services via telemedicine.
• Many women with health insurance report experiencing limitations with their insurance in the past 12 months. One in five (21%) women with Medicaid say that their plan did not cover care they thought was covered, or paid less than expected, compared to one-third (34%) of women with employer-sponsored coverage.
• More than one in four (27%) women ages 18-64 report having had problems paying medical bills in the past 12 months compared to 23% of men.
• Among women experiencing problems with medical bills in the past year, more than half (52%) have had difficulty paying for basic necessities like food, heat, or housing because of the bills, including nearly seven in ten (68%) women with low incomes.

Health Status and Use of Prescription Medications

Health Status

The majority of women ages 18-64 report being in good or excellent health, but substantial shares of women with low incomes and those with Medicaid describe their health as fair or poor.

Most women ages 18-64 (82%) rate their health as excellent, very good, or good; however, 18% of women describe their health as fair or poor, similar to other national estimates.

A higher share of women ages 36-49 (20%) and ages 50-64 (19%) rate their health as fair or poor than those ages 18-35 (16%) (Figure 1). A larger share of Black (22%) and Hispanic (20%) women report being in fair or poor health than White (16%) and Asian/Pacific Islander (13%) women. (Persons of Hispanic origin may be of any race; other groups are non-Hispanic.) Approximately three in ten women with low incomes (28%) and women with Medicaid coverage (30%) rate their health as fair or poor. (This survey defines low income as household income under 200% of the federal poverty level (FPL); higher income is 200% or more of the FPL. The federal poverty level (FPL) for an individual in 2022 is $13,590.) A higher share of women with Medicaid describes their health as fair or poor than women with employer-sponsored insurance (11%), individual insurance (13%), and the uninsured (23%).

Many women are managing ongoing health conditions or living with disabilities that impact daily life.

Half (49%) of women ages 18-64 report that they have an ongoing health condition that requires regular monitoring, medical care, or medication (Figure 2). This rate increases with age, from 41% of women ages 18-49 to 65% of women ages 50-64. Smaller shares of Hispanic (41%) and Asian/Pacific Islander (38%) women report having an ongoing health condition than White (52%) and Black (49%) women. More women with employer-sponsored insurance (46%) report having an ongoing health condition requiring regular care than do uninsured women (36%). Women with Medicaid (53%) are more likely than those with employer-sponsored insurance or the uninsured to report having an ongoing condition.

Eighteen percent of women ages 18-64 report having a disability or chronic disease that keeps them from participating fully in work, school, housework, or other activities (Figure 3). Women ages 50-64 are more likely than those ages 18-49 to report having a disability or chronic disease (24% vs. 15%). Lower shares of Hispanic (14%) and Asian/Pacific Islander (6%) women have a limiting disability or chronic condition than do White (18%) and Black (22%) women. Women with Medicaid coverage (30%) and those with low incomes (29%) are more likely than their counterparts to report having a disability or chronic disease.

Use of Prescription Medications

Almost two-thirds of women ages 18-64 take at least one prescription medication, including birth control pills, on a regular basis.

Women may take prescription medications to treat or manage chronic conditions and acute illnesses or to prevent pregnancy. Sixty-three percent of women report taking at least one prescription medicine, including birth control pills, on a regular basis (Figure 4).

Prescription medication use increases with age, with 58% of women ages 18-49 taking at least one on a regular basis compared to 73% of women ages 50-64. White women (68%) are more likely than Asian/Pacific Islander (49%), Hispanic (55%), and Black (58%) women to take a prescription medication on a regular basis. As may be expected, a higher share of women in fair or poor health report taking a prescription medication regularly than women in excellent or very good health (76% vs. 54%). Even though more uninsured women (23%) rate their health as fair or poor than insured women (17%), a higher share of insured women (65%) (data not shown) report taking take a prescription medication than uninsured women (43%). One explanation for this could be that uninsured women have less access to health care and affordable medications than those with insurance, which could result in lower use of prescription medications.

Differences Between Women and Men

In measures of health status and prescription drug use, there are some differences and some similarities between women and men.

More women than men report having an ongoing health condition that requires regular monitoring, medical care, or medication (49% vs. 43%), or taking at least one prescription medication on a regular basis (63% vs. 48%) (Figure 5). The share of women and men who rate their health status as fair or poor is similar (18% and 20%, respectively), as is the share who report having a disability or chronic disease that keeps them from participating fully in work, school, housework, or other activities (18% and 17%, respectively). (People of all genders, including non-binary people, were asked these questions; however, there are insufficient data to report on non-cisgendered people.)

Accessing Health Care

Sources of Care

While the vast majority of women have a regular provider they turn to for routine care, only half of uninsured women have a usual source of care.

Having a usual source of health care is associated with increased use of preventive care and better health outcomes. Eighty-two percent of women have a regular doctor or health care provider they see when they are sick or need routine care. The share of women who have a regular provider increases with age (Figure 6). Hispanic women (79%) are slightly less likely than White (83%) and Black (84%) women to have a usual source of care. Fewer women who are uninsured (52%) and those with low incomes (79%) report having a usual source of care than their counterparts. Women who live in a state that expanded Medicaid are more likely to have a usual source of care than women in states that have not expanded Medicaid (83% vs. 79%) (data not shown). Ten percent of women with a disability or an ongoing health condition do not have a regular doctor or health care provider (data not shown).

Sites of Care

Most women obtain their health care at doctors’ offices, but health centers and clinics are common sites of care for underserved communities, particularly for uninsured women, those with Medicaid, and Hispanic women.

Eighty-two percent of women have a regular doctor or place they usually go for health care. Among them, eight in ten (79%) report that they usually obtain their care at a doctor’s office (Figure 7). Ten percent obtain care at an urgent care or retail clinic; 8% usually go to a health center or school-based clinic; and 3% cite the emergency room or some other place as their usual site of care.

More women ages 50-64 (88%), White (84%) and Asian/Pacific Islander (83%) women, women with higher incomes (85%), and those with private health insurance (86%) rely on an office-based physician for their regular care than their counterparts (Table 1). (Private health insurance includes employer-sponsored insurance and individually purchased insurance.) Health centers and clinics play a larger role as sites of care for women ages 18-49 (10%), Hispanic women (19%), women with low incomes (15%), those with Medicaid (17%), and uninsured women (25%) than their counterparts.

Check-ups

Regular provider visits give people an opportunity to talk with clinicians about a broad range of issues, including preventing illness, the role of lifestyle factors on health, and management of chronic illnesses. Under the Affordable Care Act, most health plans must cover at least one annual check-up/well-woman visit, which can include assessments of diet and physical activity, preconception care, and cancer screenings.

Overall, women have more interactions with and connections to the health care system than men. Higher shares of women than men report having a usual source of care and have visited a health care provider in the past two years.

Women are more likely than men to report having a regular place of care (87% vs. 77%) and a regular doctor or health care provider (82% vs. 71%) (Figure 8). Ninety-five percent of women say they have visited a health care provider in the past two years and among them, 77% have had a check-up, compared to 88% and 72% of men, respectively. (People of all genders, including non-binary people, were asked these questions; however, there are insufficient data to report on non-cisgendered people.)

While most women ages 18-64 have visited a doctor in the past two years and had a check-up, rates are lower among younger women and uninsured women.

Nearly all women (95%) have seen a doctor or health care provider in the past two years (Figure 8). However, fewer women ages 18-35 (92%), Hispanic (92%), and uninsured women (78%) have visited a doctor in the past two years than their counterparts.

Among women who have seen a health care provider in the past two years, more than three-quarters (77%) have had a general check-up/well-woman visit during that period. However, a smaller share of uninsured women (61%) and women with low incomes (74%) had a recent check-up than their counterparts. A smaller share of women ages 18-35 had a check-up than women ages 50-64 (72% vs. 85%), and more Black women than White women had one (83% vs. 77%).

Telemedicine

Two years into the pandemic, most women ages 18-64 (60%) say they have had a telehealth visit (Figure 9).

Smaller shares of younger women report having had a telehealth visit in the past two years and fewer than half of uninsured women say they have had one. Women living in rural areas are less likely than those living in urban areas to have had a telehealth visit with a health care provider (49% vs. 62%) in the past two years, a general trend that has been documented in other studies. This could be due, in part, to patient preferences as well as more limited access to reliable high-speed internet and technology among those living in rural areas than in urban/suburban areas, as has been documented by other research. Slightly fewer women living in Medicaid non-expansion states have had a telehealth visit than those in expansion states (58% vs. 62%). There were no statistically significant differences by race/ethnicity (data not shown).

Among women ages 18-64 who had a telehealth visit in the past two years, nearly two in ten (18%) say the primary purpose for their visit was for an annual check-up or well-visit, minor illness or injury, or mental health services (Table 2). The next most common reasons included management of a chronic condition (16%), another health concern not listed (13%), COVID-related symptoms (10%), and gynecological or sexual health issues (5%).

Primary reasons for telehealth visits often vary by age. Among women ages 18-25 who had a telehealth visit in the past two years, three in ten (29%) say the primary purpose for their most recent telehealth visit was mental health services, compared to just 10% of women ages 50-64. Almost one-quarter (23%) of women ages 50-64 say their most recent telehealth visit was for management of a chronic condition, compared to 8% of women ages 18-25. Among women ages 18-49 who had a telehealth visit in the past two years, 2% say prenatal or postpartum care was the primary purpose for the most recent telehealth, including 2% of those ages 18-25, 4% of those ages 26-35, and 5% of those ages 36-49 (data not shown).

Most women (70%) say the quality of care they received at their most recent telehealth visit was similar to the quality of in-person care, with 12% saying their telehealth care was better and 17% saying it was worse than in-person care (Figure 10). Larger shares of women with low incomes (18%) and those who are uninsured (22%) say their telehealth care was better than in-person care than women with higher incomes (10%) and those with private insurance (10%). Two percent of women who had a telehealth visit in the past two years say they have not had an in-person visit for this type of care.

When looking at specific types of services provided via telehealth, one in five (19%) women receiving telehealth services for a gynecological or sexual health issue or mental health services say the care they received via telehealth was better than in-person care (Figure 11). Conversely, about one in five women say their telehealth care for treatment of minor illnesses and injuries (22%) and COVID-related symptoms (21%) was worse than in-person care. Among women ages 18-49 who had a telehealth visit in the past two years and whose most recent visit was for prenatal or postpartum care, 18% said the quality of care was better than the quality of in-person care, 65% said it was the same, and 17% said it was worse (data not shown).

Health Coverage and Costs

Insurance

More insured women than men report having experienced problems with their insurance covering needed health services or medications in the past 12 months.

Thirty-one percent of women ages 18-64 with health coverage (employer-sponsored insurance, individual insurance, or Medicaid) report that in the past 12 months, they or a family member received care from a doctor, hospital, or lab they thought was covered, but their health plan did not cover the bill at all, or paid less than they expected, compared to 26% of men who say the same (Figure 12). More insured women than men say their plan would not cover a prescription medication or required a very expensive copay or coinsurance for it (29% vs. 21%). Women were slightly more likely than men to say that in the past 12 months, their plan would not cover a test or scan that their doctor recommended (20% vs. 17%).

These gender differences could be due to several factors, including that women are more likely than men to have visited a health care provider in the past two years, to take prescription medication on a regular basis, and to have an ongoing health condition that requires regular monitoring, medical care, or medication (see “Health Status and Use of Prescription Medications” section above).

Some of these insurance coverage problems vary by type of insurance plan (Table 3).

For example, among women, more women with private insurance (individual or employer-sponsored plans) (34%) than women with Medicaid (21%) say their plan did not cover medical care they thought was covered, or paid less than expected, in the past 12 months. A higher share of women with individual coverage (35%) than women with employer-sponsored coverage (27%) or Medicaid (29%) say that their plan did not cover a prescription medication or required high cost sharing for it. Women with individual coverage (28%) are also more likely than women with employer-sponsored coverage (20%) or Medicaid (21%) to report their plan did not cover a test or scan their doctor recommended in the past 12 months.

Insurance companies sometimes deny coverage of prescription medications or require large co-payments, but many women are not aware that they can appeal coverage decisions.

The ACA requires that most private health insurance plans have an appeals process for coverage determinations and claims. State Medicaid programs are also required by federal law to have an appeals process. Among the 65% of insured women who take a prescription medication on a regular basis, only four in ten (40%) are aware that they can ask their insurance company to reconsider covering the cost of a prescription, known as filing an appeal (Figure 13).

Knowledge of this option varies by income level and insurance type. More low-income women with insurance who take a prescription medication are aware that they can file an appeal with their health plan than higher-income women (44% vs. 38%). Women with Medicaid are also more likely than those with employer-sponsored insurance to know that this is an option (45% vs. 38%).

Many women who take a prescription medication say their out-of-pocket costs for those medications have increased over the past year.

Among the nearly two-thirds (63%) of women ages 18-64 who take a prescription medication on a regular basis, almost one-third (32%) say their out-of-pocket costs for those medications have increased compared to 12 months ago (Figure 14). Over half (54%) say their out-of-pocket costs have stayed the same, 6% say their costs have decreased, and 8% say they do not know.

Changes in out-of-pocket costs vary by type of insurance coverage. Fifty-five percent of women who are uninsured and who take a prescription medication say that their costs have increased compared to 12 months ago, compared to 34% of women with private insurance and 17% of those with Medicaid. Most women with private insurance (53%) and Medicaid (63%) say that their out-of-pockets costs for their prescription medications has not changed in the past year. Notably, twice as many women with Medicaid (16%) who take a prescription medication say they do not know how their out-of-pocket costs for those medications has changed, compared to 8% of all women. (Four percent say they were not taking any of these medications 12 months ago; they are not included in this analysis.)

Medical Bills

While the ACA has addressed some financial barriers to accessing health care, many women, including those with insurance, still report problems paying medical bills. Some women incur significant medical expenses because of an unexpected diagnosis such as cancer, or an illness or injury that limits their ability to work and earn income to pay off bills. Costly medical bills can also arise after receiving care from an out-of-network provider, commonly referred to as surprise medical bills. (The No Surprises Act, which took effect in January 2022, establishes new federal protections against surprise medical bills.)

More than one in four women ages 18-64 report having had problems paying medical bills in the past year, with higher rates among uninsured women and women in poorer health. (Poorer health refers to those who report being in fair or poor health.)

Twenty-seven percent of women report that they or a household family member has had problems paying medical bills in the past 12 months, a slight increase from 2020 (24%). Problems paying medical bills are more common among women than men (23%).

Outstanding medical bills are more common among uninsured and women with low incomes and those in poorer health (Figure 15). This includes more than two in five (42%) uninsured women and women in poorer health, and almost two in five women with low incomes (37%). Approximately one-third of women who live in states that did not expand Medicaid (34%), Black (32%) and Hispanic women (32%) also report having trouble paying medical bills in the past 12 months. Women who live Medicaid expansion states are less likely than those in non-expansion states to have had trouble paying medical bills (data not shown). More women with children report problems paying medical bills in the past 12 months than do women without children (32% vs. 25%).

Among women who report having had problems paying medical bills in the past 12 months, one-third (34%) say it was because they or someone in their family lost a job, income, or unemployment benefits. Seventeen percent say it was because they or someone in their family had health expenses related to COVID-19. The majority (66%) of women who say they have had trouble paying medical bills in the past 12 months report that it is due to some other reason (Figure 16).

Medical bills can have serious consequences for people’s financial well-being and ability to afford basic necessities. Although more women than men report having problems paying medical bills in the past 12 months, women and men experience financial consequences because of the bills at similar rates.

Among the 27% of women and 23% of men who have had problems paying medical bills in the past 12 months, similar shares have had to set up a payment plan to pay off the bill (65% of women and 68% of men); used up all or most of their savings (62% of women and 69% of men); been contacted by a collection agency (61% of women and 56% of men); and had difficulty paying for basic necessities (52% of women and 53% of men) (Table 4). However, a higher share of men than women with recent problems paying medical bills report borrowing money from family or friends (53% vs. 42%).

For women, the financial consequences of medical bills vary by income level (Figure 17).

Among the 27% of women who report having trouble paying medical bills in the past 12 months, women with low incomes are more likely than women with higher incomes to report having difficulty paying for basic necessities such as food, heat, or housing (68% vs. 39%); having been contacted by a collection agency (70% vs. 53%); or borrowing money from family or friends (56% vs. 29%). Women with higher incomes are more likely than women with low incomes to have had to set up a payment plan with a doctor or hospital (71% vs. 59%).

Conclusion

Women’s access to and use of health care services has an impact on their health outcomes. Most women report having a regular source of care and having had a recent doctor’s visit. However, connections to the delivery system are more tenuous for uninsured women and women with low incomes, who are less likely to report a recent visit or regular place of care. Most women report they have had a general check-up/well-woman visit in the past two years, but like other measures on access to care, rates are lower among uninsured women and those with low incomes. When looking at gender differences, women are more likely to have regular connections with the health care system than men.

The COVID-19 pandemic has changed how some people access health care, particularly a shift from in-person care to increased use of telehealth. Federal and state policy changes and market forces also contributed to the growth in the use of telehealth. Most women say they have used telehealth in the past two years, and most of them rate the quality of care they received as comparable to an in-person visit. This pattern is consistent for all types of telehealth services our survey asked about, ranging from check-ups to mental health care services. While many women do not express concerns about quality differences between telehealth and in person care, the extent to which this newer modality will continue to be utilized and grow will also depend on the reimbursement policies that private plans and public payors adopt following the end of the pandemic emergency.

Despite the role that health insurance plays in helping people afford health care and reducing patients’ financial risks when they need routine care, get sick, or need to be hospitalized, scope of coverage and affordability can still be challenging for many women with insurance coverage. A sizeable minority of women with insurance report having problems using their health plan, such as their plan not paying for health care they thought was covered but was not, or their plan not covering their prescription drugs or requiring high cost sharing for them. Many people say they have had problems paying medical bills in the past year, bur rates are higher among women than men. While the ACA strengthened access to coverage and offers protection from some out-of-pocket health care costs, gaps remain. For many, health care and coverage are still unaffordable and represent potential barriers to needed care.

Overview

The 2022 KFF Women’s Health Survey is a nationally representative survey of 6,442 people ages 18 to 64, including 5,201 females (self-reported sex at birth) and 1,241 males, conducted from May 10, 2022, to June 7, 2022. The objective of the survey is to help better understand respondents’ experiences with contraception, potential barriers to health care access, and other issues related to reproductive health. The survey was designed and analyzed by researchers at KFF (Kaiser Family Foundation) and fielded online and by telephone by SSRS using its Opinion Panel, supplemented with sample from IPSOS’s KnowledgePanel.

This work was supported in part by Arnold Ventures. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

Questionnaire design

KFF developed the survey instrument with SSRS feedback regarding question wording, order, clarity, and other issues pertaining to questionnaire quality. The survey was conducted in English and Spanish. The survey instrument is available upon request.

Sample design

The majority of respondents completed the survey using the SSRS Opinion Panel (n=5,202), a nationally representative probability-based panel where panel members are recruited in one of two ways: (1) through invitations mailed to respondents randomly sampled from an Address-Based Sample (ABS) provided by Marketing Systems Group through the U.S. Postal Service’s Computerized Delivery Sequence. (2) from a dual-framed random digit dial (RDD) sample provided by Marketing Systems Group.

In order to have large enough sample sizes for certain subgroups (females ages 18 to 35, particularly females in the following subgroups: lesbian/gay/bisexual; Asian; Black; Hispanic; Medicaid enrollees; low-income; and rural), an additional 1,240 surveys were conducted using the IPSOS KnowledgePanel, a nationally representative probability-based panel recruited using a stratified ABS design.

Data collection

Web Administration Procedures

The majority of surveys completed using the SSRS Opinion Panel (n=5,056) and all of the surveys completed using the KnowledgePanel (n=1,240) were self-administered web surveys. Panelists were emailed an invitation, which included a unique passcode-embedded link, to complete the survey online. In appreciation for their participation, panelists received a modest incentive in the form of a $5 or $10 electronic gift card. All respondents who did not respond to their first invitation received up to five reminder emails and panelists who had opted into receiving text messages from the SSRS Opinion Panel received text message reminders.

Overall, the median length of the web surveys was 13 minutes.

Phone Administration Procedures

In addition to the self-administered web survey, n=146 surveys were completed by telephone with SSRS Opinion Panelists who are web reluctant. Overall, the median length of the phone surveys was 28 minutes.

Data processing and integration

SSRS implemented several quality assurance procedures in data file preparation and processing. Prior to launching data collection, extensive testing of the survey was completed to ensure it was working as anticipated. After the soft launch, survey data were carefully checked for accuracy, completeness, and non-response to specific questions so that any issues could be identified and resolved prior to the full launch.

The data file programmer implemented a “data cleaning” procedure in which web survey skip patterns were created in order to ensure that all questions had the appropriate numbers of cases. This procedure involved a check of raw data by a program that consisted of instructions derived from the skip patterns designated on the questionnaire. The program confirmed that data were consistent with the definitions of codes and ranges and matched the appropriate bases of all questions. The SSRS team also reviewed preliminary SPSS files and conducted an independent check of all created variables to ensure that all variables were accurately constructed.

As a standard practice, quality checks were incorporated into the survey. Quality control checks for this study included a review of “speeders,” reviewing the internal response rate (number of questions answered divided by the number of questions asked) and open-ended questions. Among all respondents, the vast majority (97%) answered 96% or more of the survey questions they received, with no one completing less than 91% of the administered survey (respondents were informed at the start of the survey that they could skip any question).

Weighting

The data were weighted to represent U.S. adults ages 18 to 64. The data include oversamples of females ages 18 to 35 and females ages 36 to 64. Due to this oversampling, the data were classified into three subgroups: females 18 to 35, females 36 to 64, and males 18 to 64. The weighting consisted of two stages: 1) application of base weights and 2) calibration to population parameters. Each subgroup was calibrated separately, then the groups were put into their proper proportions relative to their size in the population.

Calibration to Population Benchmarks

The sample was balanced to match estimates of each of the three subgroups (females ages 18 to 35, females ages 36 to 64, and males ages 18 to 64) along the following dimensions: age; education (less than a high school graduate, high school graduate, some college, four-year college or more); region (Northeast, Midwest, South, West); and race/ethnicity (White non-Hispanic, Black non-Hispanic, Hispanic-born in U.S., Hispanic-born Outside the U.S., Asian non-Hispanic, Other non-Hispanic). The sample was weighted within race (White, non-Hispanic; Black, non-Hispanic; Hispanic; and Asian) to match population estimates. Benchmark distributions were derived from 2021 Current Population Survey (CPS) data.

Weighting summaries for females ages 18 to 35, females ages 36 to 64, and males ages 18 to 64 are available upon request.

Finally, the three weights were combined, and a final adjustment was made to match the groups to their proper proportions relative to their size in the population (Table 1).

Margin of Sampling Error

The margin of sampling error, including the design effect for subgroups, is presented in Table 2 below. It is important to remember that the sampling fluctuations captured in the margin of error are only one possible source of error in a survey estimate and there may be other unmeasured error in this or any other survey.

KFF Analysis

Researchers at KFF conducted further data analysis using the R survey package, including creating constructed variables, running additional testing for statistical significance, and coding responses to open-ended questions. The survey instrument is available upon request.

Rounding

Some figures in the report do not sum to totals due to rounding. Although overall totals are statistically valid, some breakdowns may not be available due to limited sample sizes or cell sizes. Where the unweighted sample size is less than 100 or where observations are less than 10, figures include the notation “NSD” (Not Sufficient Data).

Statistical significance

All statistical tests are performed at the .05 confidence level. Statistical tests for a given subgroup are tested against the reference group (Ref.) unless otherwise indicated. For example, White is the standard reference for race/ethnicity comparisons and private insurance is the standard reference for types of insurance coverage. Some breakouts by subsets have a large standard error, meaning that sometimes even large differences between estimates are not statistically different.

A note about sex and gender language

Our survey asked respondents which sex they were assigned at birth, on their original birth certificate (male or female). They were then asked what their current gender is (man, woman, transgender, non-binary, or other). Those who identified as transgender men are coded as men and transgender women are coded as women. While we attempted to be as inclusive as possible and recognize the importance of better understanding the health of non-cisgendered people, as is common in many nationally representative surveys, we did not have a sufficient sample size (n >= 100) to report gender breakouts other than men and women with confidence that they reflect the larger non-cisgender population as a whole. The data in our reproductive health reports use the respondent’s sex assigned at birth (inclusive of all genders) to account for reproductive health needs/capacity (e.g., ever been pregnant) while the data in our other survey reports use the respondent’s gender.

Key Findings

• While most of the public continue to have confidence in the benefits of childhood vaccines for measles, mumps, and rubella, the experience of the COVID-19 pandemic and debates over vaccine requirements and mandates appear to have had an impact on public attitudes towards MMR vaccine requirements for public schools. The latest KFF COVID-19 Vaccine Monitor survey finds that about seven in ten adults (71%) say healthy children should be required to get vaccinated for MMR in order to attend public schools, down from 82% who said the same in an October 2019 Pew Research Center poll. Almost three in ten (28%) now say that parents should be able to decide not to vaccinate their school-age children, even if this creates health risks for others, up from 16% in 2019. Among Republicans and Republican-leaning independents, there has been a 24 percentage-point increase in the share who hold this view (from 20% to 44%).
• With COVID-19 cases rising across the country, just about a third of adults say they are worried they will get seriously sick from COVID-19, though nearly half of the public say they are worried about an increase in COVID-19 cases and hospitalizations in the U.S. this winter. As previous KFF surveys have repeatedly found, Black and Hispanic adults continue to be more concerned about the pandemic compared to White adults, with about two-thirds of Black adults (68%) and Hispanic adults (69%) saying they are worried about an increase in cases and hospitalizations this winter, compared to about four in ten White adults who say the same. Older adults are more likely than those under age 65 to say they are worried they will get seriously sick from COVID-19 (43% vs. 34%) and that cases and hospitalizations will rise this winter (60% vs. 46%).
• As the country faces a “tripledemic”, with a surge in flu and RSV (respiratory syncytial virus) cases accompanying the rise in COVID-19 cases, many parents are now concerned about not one, but all three of these viruses. About half of parents are worried their child will get seriously sick from COVID-19 or the flu. A slight majority of parents (56%) say they are worried their child will get seriously sick from RSV – rising to more than seven in ten parents with a child under the age of 5 (73%), an age group that is particularly vulnerable to RSV.
• Though many no longer see COVID-19 as a uniquely urgent threat, public health officials continue to encourage vaccination and emphasize the importance of the updated bivalent booster to help prevent serious illness and death from COVID-19, particularly in light of holiday gathering and travel. However, public uptake of the updated booster is relatively tepid, with just about one in five adults saying they have already gotten it. Democrats (38%) and adults ages 65 and older (39%) have been more eager, with about four in ten saying have already gotten the updated COVID-19 booster which has been available since September. Fewer young adults under the age of 30 (11%) and Republicans (12%) report having gotten an updated booster dose.
• Though public health officials have stressed the importance of the updated COVID-19 booster for older adults who are more vulnerable to complications from a COVID infection, more than half of adults ages 65 and older have not yet gotten the updated booster. About a third (36%) of vaccinated adults ages 65 and older who have not yet gotten the bivalent booster say they don’t think they need it (36%) and a similar share say they don’t think the benefit of the updated booster is worth it.
• Vaccinated Republicans and Republican-leaning independents are particularly skeptical of the value of the updated booster with about two-thirds of those who have not yet gotten it saying they don’t think they need it (64%) and that the benefit is not worth it (61%) while Democrats are most likely to say they have been too busy or haven’t had the time to get the update booster (51%).

The Impact Of COVID-19 On Attitudes Towards Other Childhood Vaccines

Despite the politicization of the COVID-19 vaccine and decreasing levels of trust in the FDA and CDC, most adults (85%) say the benefits of childhood vaccines for measles, mumps, and rubella (MMR) outweigh the risks, with little change from the share who said the same in a Pew Research Center poll in 2019 (88%). Though there were no significant differences across partisans in 2019, our survey finds that Republicans and Republican-leaning independents are now less likely than their Democratic counterparts believe the benefits of MMR vaccines outweigh the risks (83% vs. 91%).

While most parents of children under age 18 (80%) say they think the benefits of childhood MMR vaccines outweigh the risks, about one in six parents (17%) think the risks of these vaccines outweigh the benefits. Among adults who have not gotten vaccinated for COVID-19, about one in four (26%) say the risks of childhood vaccines for measles, mumps, and rubella outweigh the benefits. Nonetheless, it remains notable that even among adults who have not gotten the COVID-19 vaccine, most (70%) say the benefits of childhood MMR vaccines outweigh the risks.

While confidence in the benefits of childhood MMR vaccines remains high, the debate over COVID-19 vaccine mandates may have had some spillover effects on attitudes towards requiring MMR vaccines for children attending public school. Currently, all states and the District of Columbia require children to be vaccinated against certain diseases, including measles and rubella, in order to attend public schools, though exemptions are allowed in certain circumstances. Yet, there has been a notable decrease since 2019 in the share of adults who say “healthy children should be required to be vaccinated (for MMR) in order to attend public schools because of the potential risk for others when children are not vaccinated,” with 71% saying they should be required to do so, an 11 percentage point decrease from a October 2019 Pew Research Center poll. Almost three in ten (28%) now say parents “should be able to decide not to vaccinate their children, even if that may create health risks for other children and adults,” an increase from 16% in 2019.

This decrease in support for MMR vaccine requirements for children in public schools is driven by Republicans and Republican-leaning independents – just a slight majority of Republicans (56%) say healthy children should be required to be vaccinated to attend public schools, a 23 percentage-point decline from 2019 when about eight in ten expressed support for such a requirement. More than four in ten Republicans and Republican-leaning independents (44%) now say that parents should be able to decide not to vaccinate their children, up from 20% in 2019. This compares to 11% of Democratic-leaning parents who say the same, a share that has held steady since 2019.

Among parents of children under age 18, about two-thirds (65%) think healthy children should be required to be vaccinated to attend public schools, down from 76% who said the same in 2019. One-third (35%) of parents now believe parents should be able to decide not to vaccinate their children, up from 23% in 2019.

While we cannot know the pre-pandemic attitudes that adults who are currently not vaccinated for COVID-19 held about childhood MMR vaccines, most (63%) of these adults unvaccinated for COVID-19 say that parents should be able to decide not to vaccinate their children, even if that creates health risks for children and adults. Just about four in ten (37%) adults who are not vaccinated for COVID-19 say that healthy children should be required to be vaccinated in order to attend public school.

COVID-19 And Other Winter Illnesses

With reports of COVID-19 cases increasing across the country, just about a third of adults (36%) say they are worried that they will get seriously sick from COVID-19, similar to the share which expressed this concern in January (34% worried) amidst the initial omicron surge in the U.S., but up from November 2021 (30% worried) before the omicron variant became widespread. However, about half of the public (49%) say they are worried that there will be an increase in COVID-19 cases and hospitalizations this year. Adults ages 65 and older, who are more vulnerable to negative outcomes from a COVID-19 infection, are more likely than younger adults to express worry about a winter COVID-19 surge (60% vs. 46%) and to worry that they will get seriously sick from the virus (43% vs. 34%).

As previous KFF surveys have found time and time again, people of color continue to be more concerned about the pandemic compared to White adults. The December KFF COVID-19 Vaccine Monitor survey finds that about two-thirds of Black adults (68%) and Hispanic adults (69%) say they are very worried about an increase in COVID-19 cases and hospitalizations this winter, compared to about four in ten White adults (39%) who express the same concern. Black and Hispanic adults (49% and 60%, respectively) are also more likely than White adults (26%) to worry that they will personally get seriously sick from the virus.

Worries About COVID-19 And Other Winter Viruses In Children

This winter has not only brought reports of increasing COVID-19 cases, but also widespread reports of a surge in flu and RSV cases, particularly among children. In a sign that COVID-19 is changing from being a singular concern to part of the landscape of different illnesses people worry about, parents’ worries about their children getting sick from COVID this winter are about on par with their worries about other viruses like flu and RSV. About half of parents (47%) say they are “very” or “somewhat” worried that their children will get seriously sick from COVID-19 and a similar share (51%) say they are worried their children will get seriously sick from the flu. A slight majority of parents (56%) say they are worried their child will get seriously sick from RSV – rising to 73% of parents with children under the age of 5, who are particularly vulnerable to RSV. Notably, despite half of parents saying they are worried their child may get seriously sick from the flu, just a third of parents (34%) say their child has gotten a flu shot for the current flu season.

Uptake Of The Updated Bivalent COVID-19 Booster

Although for many people COVID-19 may be less of an urgent concern this winter, public health officials continue to emphasize the importance of boosters in reducing the risk of serious illness and death particularly among the most vulnerable. Despite this, the public’s response to the new bivalent booster has been somewhat lackluster. About four in ten adults say they have either received the updated bivalent COVID-19 booster dose (22%)¹ , which has been available since September, or say they plan to get the new booster as soon as possible (16%). About one in ten adults say they want to “wait and see” before getting the new booster (12%), while a similar share (13%) say they will only get it if they are required to do so. A further 9% say they will definitely not get the new updated booster while about one in four adults (27%) are unvaccinated or only partially vaccinated, and therefore not eligible for the updated bivalent booster dose.

KFF’s September COVID-19 Vaccine Monitor survey, fielded shortly after the new updated booster was made available, found that more than a third of older adults ages 65 and older said they intended to get the updated booster as soon as possible. This month’s survey finds that many of these older adults remain eager, with four in ten adults ages 65 and older (39%) saying they have already gotten the updated COVID-19 booster while 16% say they will do so as soon as they can. However, this still leaves more than half of older adults, who are more vulnerable to complications from a COVID infection, without the protection of the updated booster.

Democrats also seem eager to get the updated booster with about four in ten (38%) saying they have already done so. Indeed, Democrats are three times as likely as Republicans to report having already gotten the updated COVID-19 booster (38% vs. 12%). Notably, about three in ten Republicans say they will only get the updated booster if they are required to do so (12%) or say they will “definitely not” get the new COVID-19 booster dose (18%). A further 37% of Republicans are unvaccinated or only partially vaccinated and therefore not eligible for the new updated COVID-19 booster dose.

Vaccinated adults who have not yet gotten a dose of the bivalent COVID-19 booster cite a variety of reasons for not getting the updated booster; about four in ten (44%) say they do not think they need it and about a third (37%) say they do not think the benefit is worth it. About a third (36%) say they have been too busy or have not had the time to get it, while about one in four (23%) say they have not gotten the updated booster because they had bad side effects from a previous COVID-19 vaccine dose. About one in six (17%) vaccinated adults who have not gotten the updated booster say they have not done so because they are waiting to see if COVID-19 cases increase in their area, while 12% say they are waiting until before they travel or see vulnerable family and friends to get the updated booster.

Though public health officials have stressed the importance of the updated COVID-19 booster for older adults, who are more vulnerable to complications from a COVID infection, about one third (36%) of vaccinated adults ages 65 and older who have not yet gotten the booster say they don’t think they need it (36%) and a similar share say they don’t think the benefit of the updated booster is worth it. About one in four (23%) vaccinated adults ages 65 and older say they have not gotten the updated booster because they have been too busy or have not had time to get it yet.

Notably, at least six in ten vaccinated Republicans or Republican leaning independents who have not yet gotten the updated booster say they haven’t done so because they don’t think they need it (64%) or do not think the benefit is worth it (61%). Among vaccinated Democrats or Democratic-leaning independents who have not yet gotten the updated booster, the most common reason for not yet doing so is having been too busy or not having the time to get it (51%).

About one in four parents of teenagers ages 12 to 17 say their child has already gotten the updated COVID-19 booster (16%) or that they will definitely be doing so (8%). A further 18% say their teen will probably get the update booster. Notably, about four in ten parents of teenagers say their 12-17 year old is not vaccinated for COVID-19 and therefore not eligible to get the updated bivalent booster.

Among parents of younger children between the ages of 5 and 11, six in ten (61%) say their child is unvaccinated and therefore not eligible for the new COVID-19 booster. About one in five parents say their 5 to 11 year old has either gotten the updated booster (14%) or will definitely be doing so (7%), while a further 9% say their child will probably get the updated booster.

This KFF COVID-19 Vaccine Monitor Poll was designed and analyzed by public opinion researchers at the Kaiser Family Foundation (KFF). The survey was conducted November 29 – December 8, 2022, online and by telephone among a nationally representative sample of 1,259 U.S. adults in English (1,203) and in Spanish (56). The sample includes 1,029 adults reached through the SSRS Opinion Panel^[1] either online or over the phone (n=32 in Spanish). The SSRS Opinion Panel is a nationally representative probability-based panel where panel members are recruited randomly in one of two ways: (a) Through invitations mailed to respondents randomly sampled from an Address-Based Sample (ABS) provided by Marketing Systems Groups (MSG) through the U.S. Postal Service’s Computerized Delivery Sequence (CDS); (b) from a dual-frame random digit dial (RDD) sample provided by MSG. For the online panel component, invitations were sent to panel members by email followed by up to three reminder emails. 1,004 panel members completed the survey online and panel members who do not use the internet were reached by phone (n=25).

Another 230 (n=24 in Spanish) interviews were conducted from a random digit dial telephone sample of prepaid cell phone numbers obtained through MSG. Phone numbers used for the prepaid cell phone component were randomly generated from a cell phone sampling frame with disproportionate stratification aimed at reaching Hispanic and non-Hispanic Black respondents. Stratification was based on incidence of the race/ethnicity groups within each frame.  Respondents in the phone samples received a $15 incentive via a check received by mail, and web respondents received a $5 electronic gift card incentive (some harder-to-reach groups received a $10 electronic gift card).

The online questionnaire included two questions designed to establish that respondents were paying attention. Cases that failed both attention check questions, those with over 30% item non-response, and cases with a length less than one quarter of the mean length by mode were flagged and reviewed. Cases were removed from the data if they failed two or more of these quality checks. Based on this criterion, one case was removed.

The combined cell phone and panel samples were weighted to match the sample’s demographics to the national U.S. adult population using data from the Census Bureau’s 2021 Current Population Survey (CPS). Weighting parameters included sex, age, education, race/ethnicity, region, and education. The sample was weighted to match patterns of civic engagement from the September 2017 Volunteering and Civic Life Supplement data from the CPS and to match frequency of internet use from the National Public Opinion Reference Survey (NPORS) for Pew Research Center.  Finally, the sample was weighted to match patterns of political party identification based on a parameter derived from recent ABS polls conducted by SSRS polls. The weights take into account differences in the probability of selection for each sample type (prepaid cell phone and panel). This includes adjustment for the sample design and geographic stratification of the cell phone sample, within household probability of selection, and the design of the panel-recruitment procedure.

The margin of sampling error including the design effect for the full sample is plus or minus 4 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. Sampling error is only one of many potential sources of error and there may be other unmeasured 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.

1. KFF’s COVID-19 Vaccine Monitor data on vaccine and bivalent booster uptake is based on self-reported responses and may differ data from the Centers for Disease Control which is based on administered doses reported by specific jurisdictions and providers. ↩︎

Key Findings

PEPFAR, the U.S. global HIV program, is the largest commitment by any nation to address a single disease in history. In addition to impacts on HIV, studies have shown that PEPFAR has had broader health impacts, including in the area of maternal and child health. Whether it has also had impacts beyond health, such as in broader economic and educational gains, is less known but could have important implications for the future of the program. If found to support these other areas, it suggests that investments in a vertical health program can have knock-on effects that support broader economic development goals and improvements, a finding that has particular relevance in an era of constrained aid budgets. Here, we examine PEPFAR’s association with five non-health outcomes: the GDP growth rate per capita; the share of girls and share of boys, respectively, who are out of school; and female and male employment rates. We find that:

• PEPFAR was associated with significant, positive improvement in three of the five measures assessed over the 2004-2018 period, while findings for the other two outcomes were inconclusive.

• PEPFAR countries experienced an increase in the GDP per capita growth rate that was greater than what would otherwise be expected in PEPFAR’s absence. Specifically, the growth rate was 2.1 percentage points higher over the period. This effect was greater in countries with “Country Operational Plans,” which engage in more intensive planning and generally have greater financial investment from PEPFAR.
• In addition, the share of girls of primary school age who were not in school declined significantly over the period, falling by more than 9 percentage points, an effect that was greater in COP and high investment countries. This result was also found for boys – the share out of school fell by 8 percentage points.
• The was no significant effect detected on labor force participation for either females or males over the period.
• Overall, these findings further contribute to the evidence base that PEPFAR’s investments have been correlated with positive, non-health outcomes.

Introduction

PEPFAR is the largest commitment by any nation to address a single disease. Since its launch in 2003, the U.S. government has provided close to $90 billion in bilateral assistance to address HIV in low and middle income countries (LMICs), and PEPFAR has been credited with saving millions of lives and helping to change the trajectory of the global HIV epidemic. In a prior analysis, we found that PEPFAR has contributed to large, significant reductions in all-cause mortality, suggesting a mortality effect beyond HIV.¹  More recently, we found that PEPFAR has had significant, positive, health spillover effects in the area of maternal and child health, including reductions in maternal and child mortality and increases in childhood immunization rates.² 

Whether PEPFAR has also had any spillover effects beyond health, however, has been less studied, and, while such an impact is plausible, it is not a given. On the one hand, PEPFAR, as a vertical, disease-specific initiative, was not designed to be an economic or educational program, and its goals are HIV-focused and targeted. On the other, the program has recognized that providing economic and educational support, such as in its DREAMS program focused on adolescent girls and young women, is important for addressing the drivers of the HIV epidemic (although its direct support for such interventions is limited and only began after 2014).^3 ,4  In addition, external aid may also act as a direct economic stimulus in countries, impacting their GDP.⁵   More broadly, studies have found that health investments are correlated with educational attainment and economic growth, including, for example, by enabling children to stay in school longer and by supporting adults to join and/or remain in the labor force.⁶   In this analysis, we seek to assess whether PEPFAR has had impacts beyond health by examining changes in five economic and educational outcomes in PEPFAR countries: the GDP growth rate; the share of girls and share of boys, respectively, who are out of school; and female and male employment rates (see Box). If PEPFAR is found to support these other areas, it suggests that investments in a vertical health program can have knock-on effects that support broader economic and development goals and improvements, a finding that has particular relevance in an era of constrained aid budgets.

The existing literature on PEPFAR’s impact in these areas is limited though there is evidence of such an effect. In a study of 21 countries in sub-Saharan Africa, including 10 PEPFAR focus countries and 11 control countries, Wagner, Barofsky, and Sood found that PEPFAR was associated with a significant increase in male employment, though not with female employment, between 2004 and 2010.⁷   Kim and Whang, in a study of 15 PEPFAR focus and 121 control countries, found that PEPFAR was associated with increases in the GDP growth rate in focus countries between 2003 and 2009.⁸  Finally, an analysis by the Bipartisan Policy Center found that GDP per capita and average output per worker were higher in countries with greater PEPFAR investment compared to countries with low or no investment between 2004 and 2016.⁹  Other studies have looked more generally at the relationship between HIV interventions, particularly antiretroviral therapy, and economic outcomes, though not at PEPFAR’s role specifically, and also found positive correlations.¹⁰   No studies were identified that have examined the relationship between PEPFAR and educational attainment, although several have more generally assessed the impact of HIV on the education of children, demonstrating the epidemic’s deleterious effects.¹¹ 

For the current analysis, we look at a larger set of countries and over a longer period of time than the prior analyses identified. We use a difference-in-difference quasi-experimental design to analyze the change in each of these outcomes in 90 PEPFAR countries between 2004, the first year in which PEPFAR funding began, and 2018, compared to a comparison group of 67 low- and middle- income countries (See methodology for more detail). We tested several different model specifications. Our final model controls for numerous baseline variables that may also be expected to influence these outcomes and which help to make the PEPFAR and non-PEPFAR country groups more comparable. Despite the strengths of the difference-in-difference model design, however, it is still possible that there may be other, unobservable ways in which comparison countries differed from PEPFAR countries which could account for our results. In addition, we are unable to determine causality, which could operate in either direction (e.g., better health results in greater economic growth or greater economic growth improves health), as has been noted in the broader health economics literature.¹² 

Findings

We find that PEPFAR was positively associated with three of the five economic and educational outcomes examined, while its association with the remaining two is inconclusive:

Between 2004 and 2018, PEPFAR was associated with a 2.1 percentage point increase in the GDP growth rate per capita over the period, compared to what would have been expected in the absence of the program. This percentage point change translates into a 45.7% increase in the GDP per capita growth rate. Looking at the broader trend, prior to PEPFAR’s initiation, the GDP per capita growth rate in comparison countries was generally higher than the rate in PEPFAR countries. This pattern began to change just a few years after PEPFAR’s initiation, near the time of the 2008 global financial crisis, when the growth rate in comparison countries fell below that of PEPFAR countries. While it is possible that the financial crisis affected PEPFAR and comparison countries differently, our model is designed to control for this possibility. We also examined the period before 1999, given the volatility in the GDP growth rate in both PEPFAR and comparison countries, which appears to be influenced by a subset of outlier countries; after removing these countries from our analysis, the results remain significant (See Figure 1, Tables 5-6, and Appendix).

PEPFAR’s estimated effect on the GDP growth rate per capita over the period was even greater in “COP” countries. The increase in the GDP growth rate per capita was 2.5 percentage points in COP countries, or a 61.5% increase over the period. These countries engage in more intensive planning and programming by preparing an annual PEPFAR Country Operational Plan (COP), compared to other PEPFAR countries, and generally receive greater funding; indeed, countries with more intensive spending saw greater change than their comparisons (See Figure 1 and Tables 5-6).

PEPFAR was also associated with a decline of 9.2 percentage points in the out-of-school rate for girls of primary school over the period. This represents a decline in the share of girls not in school of 42.4%. This effect was strongest in COP countries and in countries with greater PEPFAR investment. The large time trend shows that, prior to PEPFAR, the share of girls out of school was much higher in PEPFAR countries, relative to comparison countries. Following the introduction of the program, these rates began to converge (see Figure 2, Tables 5-6, and Appendix).

Similarly, the share of boys of primary school age who were out-of-school also declined in PEPFAR countries, by 8 percentage points relative to what would be expected. This represents a decline of 43.1%. As with girls, the effect was stronger in COP countries and in countries with greater PEPFAR investment and the broader trend was similar to that of girls (see Figure 3, Tables 5-6, and Appendix).

By contrast, PEPFAR’s effect on employment rates for both females and males is inconclusive. Our findings for employment rates for females and males, respectively, were not statistically significant (we only report final results significant at the p < 0.001 level). More generally, the rates of employment for both females and males were essentially flat over the entire 1990-2018 period for PEPFAR and comparison countries (and higher for female employment in PEPFAR countries even before PEPFAR’s initiation). (see Tables 5-6 and Appendix).

Implications

Our findings confirm the prior literature demonstrating a relationship between PEPFAR and economic growth.¹³   We show that these impacts are most pronounced in COP countries, which also receive the highest levels of PEPFAR investment. These effects could be due to the direct economic stimulus of PEPFAR assistance on low and middle income economies; in general, PEPFAR funding as a share of GDP in COP countries was under one percent in most years, though in some, it ranged between 1-2%.¹⁴  We also demonstrate the impacts of PEPFAR on two measures not previously reported in the literature – a decrease in the share of girls and of boys, of primary school age, who were out-of-school. Again, PEPFAR impacts were greatest in COP countries. We do not, however, find evidence of a relationship between PEPFAR and rates of employment for females and males (our findings were not significant). While increases in both GDP growth rates and educational engagement may be expected to result in increased labor participation, such impacts may take many years before they become evident or may be influenced by other factors. Still, this area warrants further exploration.

While our findings regarding GDP growth rates per capita and educational engagement are strong, and despite the strengths of the difference-in-difference model design, it is possible that there may be other, unobservable ways in which comparison countries differed from PEPFAR countries, which could account for our results. As mentioned above, for example, it is possible that the financial crisis affected PEPFAR and comparison countries differently, although our model design attempts to control for this possibility. In addition, it is possible that patterns within a subset of countries could be driving the overall trend. Future analysis could seek to explore other factors that may contribute to these findings as well as the country-level effects for these measures. It could also further explore the different pathways that may help to explain the relationships between PEPFAR support and improved economic and educational outcomes.

Overall, these findings further contribute to the evidence base that PEPFAR’s investments have also been correlated with positive, non-health outcomes. Given tight budgets and ongoing questions about the future trajectories of global health efforts more broadly and PEPFAR specifically, such findings indicate that a large, vertical health program which has been shown to have significant health impacts, may also contribute to broader economic and development goals.

Methods

We used a difference-in-difference¹⁵ , quasi-experimental design to estimate a “treatment effect” (PEPFAR), compared to a group without the intervention (the counterfactual). The difference-in-difference design compares the before and after change in outcomes for the treatment group to the before and after change in outcomes for the comparison group. We constructed a panel data set for 157 low- and middle- income countries between 1990 and 2018. Our PEPFAR group included 90 countries that had received PEPFAR support (between 2004 and 2018). Our comparison group included 67 low- and middle-income countries that had not received any PEPFAR support or had received minimal PEPFAR support (<$1M over the period or <$.05 per capita) between 2004 and 2018.  The pre-intervention period was 1990 to 2003 and post intervention period was 2004 to 2018.  Data on PEPFAR spending by country were obtained from the U.S. government’s https://foreignassistance.gov/ database and represent U.S. fiscal year disbursements; data for other measures were obtained from the World Bank’s World Development Indicator database and the Institute for Health Metrics and Evaluation (IHME) database, unless otherwise noted. Our outcomes of interest, their definitions, and sources are listed in Table 1. Baseline variables (for 2004) and sources are listed in Table 2 and model specifications examined in Table 3. Table 4 provides baseline means for all outcome variables. Final results are presented in Tables 5-6.

We explored several difference-in-difference model specifications, compared to an unadjusted model (see Table 3). Each specification controlled for numerous baseline variables that may be expected to influence the outcome of interest to help make the non-PEPFAR group more comparable to the PEPFAR group. Baseline means for outcome variables are provided in Table 4. Final results are presented in Tables 5-6 and are from model specification #3, and significance is only reported in the analysis for results at the p<0.001 level. The appendix provides trend data for each outcome variable in PEPFAR and comparison countries over the full study period.–

Despite the strengths of the difference-in-difference design, there are limitations to this approach. While we adjusted for numerous baseline factors that could be correlated with our outcomes of interest, there may be other, unobservable factors that are not captured here. Similarly, while our baseline factors are also intended to adjust for selection bias, there may be other ways in which comparison countries differed from PEPFAR countries (and factors which influenced which countries received PEPFAR support), which could bias the estimates.–

1. Kates J, Nandakumar A, Gaumer G, Hariharan D, Crown W, Wexler A, Oum S, Rouw A, Assessing PEPFAR’s Impact: Analysis of Mortality in PEPFAR Countries, KFF, 2021. Available at: https://modern.kff.org/global-health-policy/issue-brief/assessing-pepfars-impact-analysis-of-mortality-in-pepfar-countries/. ↩︎
2. Kates J, Gaumer G, Crown W, Hariharan D, Nandakumar A, Assessing PEPFAR’s Impact: Maternal and Child Health Spillover Effects in PEPFAR Countries, KFF, 2022. Available at: https://modern.kff.org/global-health-policy/issue-brief/assessing-pepfars-impact-analysis-of-maternal-and-child-health-spillover-effects-in-pepfar-countries/. ↩︎
3. State Department, PEPFAR 2022 Country and Regional Operational Plan (COP/ROP) Guidance for all PEPFAR-Supported Countries, February 2022. Available at: https://www.state.gov/wp-content/uploads/2022/02/COP22-Guidance-Final_508-Compliant-3.pdf. ↩︎
4. State Department, PEPFAR DREAMS Guidance, March 2021. Available at: https://static1.squarespace.com/static/5a29b53af9a61e9d04a1cb10/t/611ed11ed7ee4f73abf24803/1629409569489/2021-08-17+DREAMS+Guidance+Final+March+2018+Update_PEPFAR+Solutions.pdf. ↩︎
5. Wagner Z, Barofsky J, Sood N, “PEPFAR funding associated with an increase in employment among males in ten sub-Saharan African countries,” Health Affairs, 2015 Jun, 34(6): 946-953. ↩︎
6. See, for example: Remes J, Wilson M, Ramdorai A, How investing in health has a significant economic payoff for developing economies, Brookings, July 2020, available at: https://www.brookings.edu/blog/future-development/2020/07/21/how-investing-in-health-has-a-significant-economic-payoff-for-developing-economies/;  World Bank, Human Capital Project, available at: https://www.worldbank.org/en/publication/human-capital; Piabuo S, Tieguhong J, “Health expenditure and economic growth – a review of the literature and an analysis between the economic community for central African states (CEMAC) and selected African countries,” Health Econ Rev, 2017 Dec, 7(23); Vogl T, Education and health in developing economies, Working Papers 1453, Princeton University, Woodrow Wilson School of Public and International Affairs; Bloom D, Khoury A, Kufenko V, Prettner K, “Spurring economic growth through human development: research results and guidance for policymakers,” Population and Development Review, 2021 Jun, 47(2): 377-409; Bloom D, Kuhn M, Prettner K, Health and economic growth, 2018, IZA DP No. 11939, available at: https://www.iza.org/publications/dp/11939/health-and-economic-growth; Collin M, Weil D, The effect of increasing human capital investment on economic growth and poverty: a simulation exercise, World Bank, WPS8590, 2018, available at: https://openknowledge.worldbank.org/handle/10986/30463. ↩︎
7. Wagner Z, Barofsky J, Sood N, “PEPFAR funding associated with an increase in employment among males in ten sub-Saharan African countries.” Health Affairs, 2015 Jun, 34(6): 946-953. ↩︎
8. Kim Y, Whang T, “The effects of the President’s Emergency Plan for AIDS Relief on the economies and domestic politics of focus countries”, Global Economic Review, 2017 Aug, 46(4), 441-463. ↩︎
9. Daschle T, Frist B, Building prosperity, stability, and security through strategic health diplomacy: a study of 15 years of PEPFAR, Bipartisan Policy Center, Washington DC, 2018, available at: https://bipartisanpolicy.org/download/?file=/wp-content/uploads/2019/03/Building-Prosperity-Stability-and-Security-Through-Strategic-Health-Diplomacy-A-Study-of-15-Years-of-PEPFAR.pdf. ↩︎
10. See, for example: Bor J, Tanser F, Newell M, Barnighausen T, “In a study of a population cohort in South Africa, HIV patients on antiretrovirals had nearly full recovery of employment”, Health Affairs, 2012 July, 31(7),1459-1469; Resch S, Korenromp E, Stover J, Blakley M, Krubiner C, Thorien K, Hecht R, Atun R, “Economic returns to investment in AIDS treatment in low and middle income countries,” PLoS ONE 2011 Oct, 6(10): e25310; Thirumurthy H, Galárraga O, Larson B, Rosen S, “HIV treatment produces economic returns through increased work and education, and warrants continued US support,” Health Affairs, 2012 Jul, 31(7):1470-7; McLaren Z, Bor J, Tanser F, Barnighausen T, Economic stimulus from public health programs: externalities from mass AIDS treatment provision in South Africa, September 2019, available at: https://mdsoar.org/handle/11603/23512. ↩︎
11. See, for example, Zivin JG, Thirumurthy H, Goldstein M, “AIDS treatment and intrahousehold resource allocation: children’s nutrition and schooling in Kenya,” J Public Econ, 2009 Aug, 93(7-8):1008-1015; Guo Y, Li X, Sherr L, “The impact of HIV/AIDS on children’s educational outcome: a critical review of global literature,” AIDS Care, 2012 April,24(8):993-1012. ↩︎
12. See discussions in: Bloom D, Khoury A, Kufenko V, Prettner K, “Spurring economic growth through human development: research results and guidance for policymakers,” Population and Development Review, 2021 Jun, 47(2): 377-409; Bloom D, Kuhn M, Prettner K, Health and economic growth, 2018, IZA DP No. 11939, available at: https://www.iza.org/publications/dp/11939/health-and-economic-growth; Weil D, “Health and economic growth”, ch. 03, p. 623-682 in, Handbook of Economic Growth, 2014, vol. 2: 623-682. ↩︎
13. Kim Y, Whang T, “The effects of the President’s Emergency Plan for AIDS Relief on the economies and domestic politics of focus countries”, Global Economic Review, 2017 Aug, 46(4), 441-463; Daschle T, Frist B, Building prosperity, stability, and security through strategic health diplomacy: a study of 15 years of PEPFAR, Bipartisan Policy Center, Washington DC, 2018, available at: https://bipartisanpolicy.org/download/?file=/wp-content/uploads/2019/03/Building-Prosperity-Stability-and-Security-Through-Strategic-Health-Diplomacy-A-Study-of-15-Years-of-PEPFAR.pdf. ↩︎
14. Authors’ analysis. ↩︎
15. Gertler P, Martinez S, Premand P, Rawlings L, Vermeersch C. Impact evaluation in practice, second edition. 2016, Washington, DC: Inter-American Development Bank and World Bank. Available at: https://openknowledge.worldbank.org/handle/10986/25030. ↩︎