# The Equity Impact Vaccines May Have On Averting Deaths And Medical Impoverishment In Developing Countries

Angela Chang, Carlos Riumallo-Herl, Nicole Perales, Samantha Clark, Andrew Clark, Dagna Constenla, Tini Garske, Michael Jackson, Kévin Jean, Mark Jit, et al.

► To cite this version:

Angela Chang, Carlos Riumallo-Herl, Nicole Perales, Samantha Clark, Andrew Clark, et al. The Equity Impact Vaccines May Have On Averting Deaths And Medical Impoverishment In Developing Countries. Health Affairs, 2018, 37 (2), pp.316-324. 10.1377/hlthaff.2017.0861. hal-03200172

HAL Id: hal-03200172

https://hal.science/hal-03200172

Submitted on 6 May 2021

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
DOI: 10.1377/hlthaff.2017.0861
HEALTH AFFAIRS 37,
NO. 2 (2018): 316-324

This open access article is
distributed in accordance with the
terms of the Creative Commons
Attribution (CC BY 4.0) license.

By Angela Y. Chang, Carlos Riumallo-Herl, Nicole A. Perales, Samantha Clark, Andrew Clark, Dagna Constenla, Tini Garske, Michael L. Jackson, Kévin Jean, Mark Jit, Edward O. Jones, Xi Li, Chutima Suraratdecha, Olivia Bullock, Hope Johnson, Logan Brenzel, and Stéphane Verguet

The Equity Impact Vaccines May
Have On Averting Deaths And
Medical Impoverishment In
Developing Countries

**Angela Y. Chang** (angela.chang@mail.harvard.edu) was a doctor of science candidate in the Department of Global Health and Population, Harvard T. H. Chan School of Public Health, in Boston, Massachusetts, at the time this article was completed. Currently she is a postdoctoral fellow at the Institute for Health Metrics and Evaluation, University of Washington, in Seattle.

**Carlos Riumallo-Herl** was a doctor of science candidate in the Department of Global Health and Population, Harvard T. H. Chan School of Public Health, at the time this article was completed. Currently he is a postdoctoral fellow in the Department of Applied Economics, Erasmus School of Economics, in Rotterdam, the Netherlands.

**Nicole A. Perales** was a master of science student in the Department of Global Health and Population, Harvard T. H. Chan School of Public Health, at the time this article was completed. Currently she is a doctoral student at the University of California, Berkeley.

**Samantha Clark** is a PhD candidate in the Pharmaceutical Outcomes Research and Policy Program, University of Washington.

**Andrew Clark** is an assistant professor in health decision modelling, London School of Hygiene and Tropical Medicine, in the United Kingdom.

**ABSTRACT** With social policies increasingly directed toward enhancing equity through health programs, it is important that methods for estimating the health and economic benefits of these programs by subpopulation be developed, to assess both equity concerns and the programs' total impact. We estimated the differential health impact (measured as the number of deaths averted) and household economic impact (measured as the number of cases of medical impoverishment averted) of ten antigens and their corresponding vaccines across income quintiles for forty-one low- and middle-income countries. Our analysis indicated that benefits across these vaccines would accrue predominantly in the lowest income quintiles. Policy makers should be informed about the large health and economic distributional impact that vaccines could have, and they should view vaccination policies as potentially important channels for improving health equity. Our results provide insight into the distribution of vaccine-preventable diseases and the health benefits associated with their prevention.

P
overty alleviation has become
one of the most important global
development targets. The World
Bank adopted ending extreme pov-
erty by 2030 and promoting shared
prosperity as its twin goals.¹ The first of the Unit-
ed Nations' Sustainable Development Goals, to
"end poverty in all its forms everywhere,"² ac-
knowledges the importance of eliminating pov-
erty in the next fifteen years. Macroeconomic
development is not the only way to address ex-
treme poverty. International agencies and policy
makers have long recognized that high out-
of-pocket health expenditures were one of the
main reasons for household impoverishment.³,⁴
In China and India, for example, out-of-pocket
spending for health services was a primary factor
driving families into poverty.⁵⁻⁷ In 2010 the
World Health Organization reported that the

cost of health care prevents many poor people
from seeking treatment while simultaneously
pushing about 150 million care seekers into pov-
erty each year.⁸ Reducing out-of-pocket spend-
ing for health care and providing financial risk
protection is critical to preventing extreme pov-
erty. One of the targets of Sustainable Develop-
ment Goal 3 for health calls for all countries to
move toward providing health care and financial
protection to everyone through universal health
coverage, emphasizes the importance of reduc-
ing out-of-pocket spending and providing finan-
cial risk protection, and focuses on equity in
accessing health services across socioeconomic
strata.⁹

It is well recognized that vaccines have con-
tributed significantly to the improvement of pop-
ulation health in the past few decades,¹⁰ but their
nonhealth impact has been less explored. Given
the preventive nature of vaccines, they may play
a role in providing financial risk protection
by preventing illnesses and its high-cost treat-
ments, and therefore averting medical impover-
ishment. Similarly, while poor health is strongly
associated with poverty,¹¹ and the World Health
Organization's *Global Vaccine Action Plan 2011–
2020* listed equity as one of its six guiding
principles,¹² limited evidence assessing the dis-
tribution of health benefits of vaccines by socio-
economic strata exists.¹³⁻¹⁶

The objective of our study was to model
the distributional health and household eco-
nomic benefits of vaccines across socioeconomic
groups. Specifically, we aimed to provide an es-
timate of the distribution of health and financial
risk protection benefits of vaccines for ten anti-
gens in forty-one low- and middle-income coun-
tries for the vaccination period 2016–30.

Study Data And Methods

We studied vaccines that prevent diseases caused
by the following ten antigens: measles, hepatitis
B, human papillomavirus, yellow fever, *Hemo-
philus influenzae* type b, *Streptococcus pneumo-
niae*, rotavirus, rubella, *Neisseria meningitidis*
serogroup A, and Japanese encephalitis.

We looked at the effects of both routine
and campaign immunization programs. A distri-
butional analysis was conducted for each antigen
by quantifying, per income quintile, the future
health gains and household financial conse-
quences corresponding to the vaccination period
of 2016–30 in forty-one low- and middle-income
countries eligible for support from Gavi, the
Vaccine Alliance. Out of the seventy-three Gavi-
eligible countries, we included these forty-one
countries (total population: 1.52 billion) in
our analysis based on the availability of data from
the Demographic and Health Surveys after 2010
(see online appendix A2).¹⁷ The number of coun-
tries that have introduced these vaccines as well
as the average projected vaccine coverage rates
in the period 2016–30 are presented in appen-
dix A3.¹⁷ We examined health gains in terms of
the number of averted deaths and household
financial consequences in terms of the number
of averted cases of medical impoverishment—
that is, by the reduction in the number of cases
where household income would fall below the
World Bank poverty line of $1.90 per day (pur-
chasing power parity-adjusted constant 2011
international dollars) as a result of medical
expenditures.

**METHODS** A flow diagram outlining each step of our approach is presented in appendix exhibit A6.¹⁷ To estimate the number of vaccine-averted deaths for each antigen, we applied

methods described elsewhere¹⁸ for four of them:
measles, *Streptococcus pneumoniae*, rotavirus,
and human papillomavirus. We quantified the
contribution of sets of risk and prognostic fac-
tors, defined as behaviors and characteristics of a
person that can be used to estimate the likeli-
hood of contracting and dying from each disease.
This model leveraged the differences in the prev-
alence of risk and prognostic factors across
socioeconomic strata (available in the Demo-
graphic and Health Surveys) to estimate the dis-
tribution of cases and deaths.¹⁹ For example, risk
factors such as wasting and vitamin A deficiency
were used to determine how measles deaths
could be distributed, based on different preva-
lence rates observed across income groups.
A detailed explanation can be found in ap-
pendix A4.1.¹⁷

For the remaining six antigens— hepatitis B,
yellow fever, *Hemophilus influenzae* type b, rubel-
la, *Neisseria meningitidis* serogroup A, and Japa-
nese encephalitis — there is a lack of established
evidence regarding the risk and prognostic fac-
tors of their associated diseases and their distri-
bution across socioeconomic strata. Therefore,
we conservatively assumed that the cases were
distributed equally across the income quintiles.
To estimate the distribution of disease-specific
deaths, we applied two coverage gradients across
quintiles: Quintile-specific vaccine coverage
rates from the Demographic and Health Surveys
were applied to determine the number of disease
cases in the presence of vaccine programs; and
quintile-specific treatment coverage rates from
the surveys on the percentages of people seeking
care from a health care provider for events such
as diarrhea, cough, and fever were applied to
determine the number of disease-specific deaths
averted.¹⁹ Details on the prevalence and relative
risks of the risk and prognostic factors, as well as
assumptions on vaccine coverage and care access
rates, can be found in appendix exhibit A7.¹⁷

Several mathematical models have recently
been used to generate disease- and country-
specific estimates of vaccine impact. From each
of these disease-specific models,²⁰⁻²² we were giv-
en national estimates of the numbers of future
deaths and cases averted over the lifetime of
cohorts vaccinated in the period 2016–30. For
example, all of the future deaths and cases (that
is, those occurring after 2016) averted by vac-
cines administered in 2016 would be considered
to be attributable to vaccination year 2016.

Finally, we calculated the number of deaths
averted per million people vaccinated using
the number of people vaccinated in Gavi's Stra-
tegic Demand Forecast.²³

To calculate the number of cases of medical
impoverishment averted for each antigen, we

**Dagna Constenla** is an
associate scientist and the
director of Economics and
Finance in the Vaccine Access
Center of the Department of
International Health, Johns
Hopkins Bloomberg School of
Public Health, in Baltimore,
Maryland.

**Tini Garske** is a lecturer in
the MRC Centre for Outbreak
Analysis and Modelling,
Department of Infectious
Disease Epidemiology,
Imperial College London, in
the United Kingdom.

**Michael L. Jackson** is an
associate investigator at the
Kaiser Permanente
Washington Health Research
Institute, in Seattle.

**Kévin Jean** is a lecturer in
epidemiology in the
laboratoire Modélisation,
épidémiologie et surveillance
des risques sanitaires
(MESuRS), Conservatoire
national des Arts et Métiers,
in Paris, France, and a visiting
lecturer in the MRC Centre
for Outbreak Analysis and
Modelling, Department of
Infectious Disease
Epidemiology, Imperial College
London.

**Mark Jit** is a professor of
vaccine epidemiology in the
Department of Infectious
Disease Epidemiology, London
School of Hygiene and
Tropical Medicine.

**Edward O. Jones** was a
researcher in the Department
of Infectious Disease
Epidemiology, London School
of Hygiene and Tropical
Medicine, when this article
was completed.

**Xi Li** is an independent health
consultant in Manila, the
Philippines.

**Chutima Suraratdecha** is an
independent consultant in
Vienna, Virginia.

**Olivia Bullock** is a program
officer at Gavi, the Vaccine
Alliance, in Geneva,
Switzerland.

**Hope Johnson** is director of
monitoring and evaluation at
Gavi, the Vaccine Alliance.
**Logan Brenzel** is a senior program officer for economics and finance, Vaccine Delivery/ Global Development, Bill & Melinda Gates Foundation, in Washington, D.C.

**Stéphane Verguet** is an assistant professor of global health in the Department of Global Health and Population, Harvard T. H. Chan School of Public Health.

used estimates of out-of-pocket spending amounts for direct costs (treatment of the disease and transportation) derived from treatment provider costs in previously published literature,²¹,²⁴⁻²⁷ and the share of out-of-pocket spending as a percentage of total health spending from the World Bank;²⁸ and on indirect costs that reflect the forgone earnings associated with care seeking.²⁹ We defined the latter as the lost income of the main household member due to the time lost for either seeking care or taking a sick child to care (appendix exhibit A8).¹⁷ Specifically, they are defined as the product of hospitalization or outpatient duration and the hourly wage for each country. When the illness affects children, indirect costs correspond to the time needed to take a child for inpatient or outpatient care; when the illness affects adults, indirect costs correspond to the time needed to seek inpatient or outpatient care.

We simulated monthly household incomes based on each country's gross domestic product per capita (adjusted for purchasing power parity) and Gini coefficient,³⁰ and we estimated the number of households that would fall below the World Bank poverty line with the incurred costs listed above. A household is considered to be in poverty when monthly household income per capita minus the household's incurred costs falls below the World Bank poverty line of $1.90 (purchasing power parity-adjusted constant 2011 international dollars) per day. This definition avoided counting households that would have been in poverty before the costs were incurred. We then aggregated the number of cases from each country to obtain the total number of cases of medical impoverishment. These measures of household economic well-being are routinely used by the World Health Organization and the World Bank to evaluate the financial impact of health policies on households.³¹ More details can be found in appendix A4.2.¹⁷

**OUTCOMES AND SCENARIOS** For each country, we first estimated the number of potential deaths that would have occurred in each income quintile if no immunization were available (our counterfactual scenario). Second, we assessed the impact of vaccine programs on this distribution. Third, we estimated the change in the distribution of deaths by comparing the results from the first two steps.

We applied quintile-specific vaccine coverage rates derived from methods described in appendix A4,¹⁷ scaled up as projected by the Strategic Demand Forecast developed by Gavi.²³ In most countries, the lowest (poorest) quintile has the lowest vaccine coverage rate, and the highest (richest) quintile has the highest rate. By definition, each quintile represents 20 percent of the

> The poorest quintiles would gain the most from vaccine programs because they have the most to gain, in both health and economic terms.

households in the population. However, household size (for example, the number of children) may vary across quintiles. We therefore applied country- and quintile-specific total fertility rates (assumed to be constant over time) to account for the differences in the number of susceptible people in each quintile, which would affect how the disease cases were distributed. To test the influence of total fertility rates on our results, we conducted a sensitivity analysis by removing the adjustment for the total fertility rates.

We generated country-specific outcomes (listed in appendix exhibit A9),¹⁷ and we present the aggregate findings in the “Study Results” section below.

**LIMITATIONS** There were several major limitations to this study. First, our model inputs—including projected vaccine coverage rates, numbers of people vaccinated, total numbers of deaths averted, rates of health care use, and costs incurred—were obtained from various different models²⁰,²¹,²³ containing a number of important assumptions that affected our findings. An important feature of the disease models is that all diseases are considered as noncompeting risks, which means that the sum of deaths across all diseases might be greater than the actual number of all-cause deaths. We also adopted a simple static approach to understand the impact of different combinations of factors in a straightforward way. This approach has limitations, such as not accounting for herd immunity (additional protective effect at the population level when a large percentage of population becomes immune) and dynamic transmissions (the changes in the nature of disease transmissions with varying numbers of people becoming susceptible, infected, and recovered over time).³²,³³ In addition, nonspecific effects of vaccines, such as their effect on overall mortality,³⁴ as well as the timeliness of receiving the vaccines,³⁵,³⁶ were not tak-
en into account. We believe that including these factors would lead to a more skewed distribution of averted deaths and impoverishment cases toward the poor.

Second, disease-specific cases and deaths are related, even though the inputs for the relative risks of prognostic factors used here captured the impact of these factors among the healthy population unaffected by disease (that is, not among disease cases). However, given the lack of data on the relative risks of prognostic factors among cases, we had to assume that these relative risks were for cases as well.

Third, we calculated the absolute number of averted deaths and their distribution across income quintiles, not how the remaining deaths were distributed after removing the averted deaths. It is possible that the distribution of deaths not averted by vaccines was skewed toward the poor. In other words, achieving greater reduction in deaths among the poorest groups does not necessarily equate with improving health equity. We also applied the income quintiles assigned by the Demographic and Health Surveys, and we acknowledge that the living conditions of people in the poorest quintile in one country may differ vastly from those of people in the poorest quintile in another country.

Fourth, we considered the effects of vaccines only on mortality, not morbidity. The potentially large impact of Japanese encephalitis and *Neisseria meningitidis* serogroup A vaccines on reducing morbidity, such as brain damage and neuro-psychiatric sequelae, is therefore not reflected in our results.

Fifth, data on out-of-pocket expenditures and medical impoverishment by disease cause are

scarce. Therefore, we had to rely on imputed modeled data and information available for a small set of countries.

Sixth, estimated deaths and averted cases of medical impoverishment that we report correspond to the future benefits over the lifetime of cohorts vaccinated in the period 2016–30. However, we did not discount these future benefits or account for growth in simulated household incomes, even though some disease-specific benefits might occur either earlier (for example, in the cases of rotavirus and measles) or later (for example, in the cases of hepatitis B and human papillomavirus) over that lifetime.

Finally, we recognize that there may be trade-offs between keeping program costs low and achieving affordability and equity goals. While we found that vaccine programs could be more *effective* in averting deaths in the poor by a large magnitude, the programs might not be the most *cost-effective* policy—given that reaching the poorest quintiles could be substantially more expensive than reaching the richest.

## Study Results

In this section we present the projected numbers of averted deaths and medical impoverishment cases, compared to the counterfactual scenario if no immunization were available. Detailed results from the main analysis and the sensitivity analysis are presented in appendixes A5 and A6.¹⁷ All costs are expressed in 2011 international dollars.

**DEATHS AVERTED** The number of future deaths averted corresponding to the cohorts vaccinated in 2016–30 for all ten antigens in forty-one low-

## EXHIBIT 1

<table><caption>Numbers of deaths and cases of medical impoverishment averted by vaccines to be administered in 41 low- and middle-income countries, 2016–30</caption><thead><tr><td>Antigen</td><td>Deaths averted (thousands)</td><td>Number of deaths averted (per million people vaccinated)</td><td>Medical impoverishment cases averted (thousands)</td></tr></thead><tbody><tr><td>Measles</td><td>22,204</td><td>11,339</td><td>4,787</td></tr><tr><td>Hepatitis B</td><td>6,639</td><td>10,751</td><td>14,034</td></tr><tr><td>Human papillomavirus</td><td>2,522</td><td>11,990</td><td>112</td></tr><tr><td>Yellow fever</td><td>1,804</td><td>4,551</td><td>835</td></tr><tr><td>Hemophilus influenzae type b</td><td>1,242</td><td>1,998</td><td>1,054</td></tr><tr><td>Streptococcus pneumoniae</td><td>782</td><td>1,337</td><td>248</td></tr><tr><td>Rotavirus</td><td>454</td><td>819</td><td>242</td></tr><tr><td>Rubella</td><td>355</td><td>897</td><td>141</td></tr><tr><td>Neisseria meningitidis serogroup A</td><td>137</td><td>81</td><td>2,684</td></tr><tr><td>Japanese encephalitis</td><td>13</td><td>35</td><td>8</td></tr></tbody></table>

SOURCE Authors' analysis.
and middle-income countries is estimated to be approximately thirty-six million (exhibit 1). Vaccines for measles, hepatitis B, and human papillomavirus accounted for the largest share of averted deaths—61 percent, 18 percent, and 7 percent, respectively. The magnitude of deaths averted by vaccines is primarily a function of the burden of the related disease (measured as total deaths that would have occurred, estimated in the counterfactual scenario), the number of countries that were forecasted to introduce the vaccine and their national immunization coverage, the proportion of people who received care after contracting the disease, and vaccine and treatment efficacy. In addition to the large disease burden in the absence of the related vaccines, by 2016 all forty-one countries are forecasted to have introduced vaccines for measles and hepatitis B, with average coverage rates greater than 80 percent and vaccine efficacy over 85 percent.²³ Human papillomavirus vaccine has high efficacy (close to 100 percent) and is modeled to be introduced in most countries. In contrast, while *Streptococcus pneumoniae* also accounts for a large disease burden, the vaccine has a lower vaccine efficacy and thus contributed to fewer averted deaths.³⁷ Vaccines for Japanese encephalitis and *Neisseria meningitidis* serogroup A account for a small proportion of averted deaths mainly because they are regional

vaccines projected to be introduced in only a limited number of countries. While rubella has a sizable case burden, it has a low case-fatality ratio, so rubella mortality accounts for a relatively small proportion of the averted deaths.

When we examined the counterfactual scenario, we found that more deaths were projected to occur in the poorest quintile for all vaccine-related diseases. The poorest quintile had a high prevalence of risk and prognostic factors, low use of health care, and a high total fertility rate. Consequently, members of this population were more likely than others to develop a case of a vaccine-preventable disease and less likely to recover from a disease once they contracted it. Although the poorest quintiles experienced the lowest vaccine coverage rates, they enjoyed the most health benefits in terms of absolute number of averted deaths: The poorest quintile accounted for the largest share of deaths averted by all vaccines (23–34 percent), and the poorest two quintiles accounted for over half of the deaths averted by most vaccines (exhibit 2). Furthermore, our estimates suggest that the distribution of deaths averted across income quintiles would vary by vaccine. For example, 61 percent and 60 percent of deaths averted by the vaccines for human papillomavirus and *Hemophilus influenzae* type b occurred in the poorest two quintiles, compared to only 16 percent and 19 percent

EXHIBIT 2

<figure><img src="image_2.png" /><figcaption>Distribution, by income quintile, of deaths averted and cases of medical impoverishment averted by vaccines to be administered in 41 low- and middle-income countries, 2016–30</figcaption></figure>

**SOURCE** Authors' analysis. **NOTES** The antigens are ordered by total disease burden, with measles (MV) having the largest and Japanese encephalitis (JE) the smallest disease burden. HepB is hepatitis B. HPV is human papillomavirus. YF is yellow fever. Hib is *Hemophilus influenzae* type b. SP is *Streptococcus pneumoniae*. RV is rotavirus. R is rubella. NmA is *Neisseria meningitidis* serogroup A.
in the richest two quintiles, respectively. In comparison, the distribution of deaths averted by the Japanese encephalitis vaccine was flatter: 44 percent of averted deaths occurred in the poorest two quintiles, compared to 36 percent in the richest two quintiles. The ratio of averted deaths between the poorest two and the richest two quintiles captures these differences in a comparable metric: The ratio ranged from 3.8 for human papillomavirus to 1.2 for Japanese encephalitis (exhibit 3).

To consider the relative effectiveness of vaccine programs, we estimated the number of deaths averted per million people vaccinated. The results varied widely by vaccine, from fewer than 100 deaths averted per million people vaccinated for Japanese encephalitis and *Neisseria meningitidis* serogroup A to more than 10,000 deaths averted per million people vaccinated for human papillomavirus, measles, and hepatitis B (exhibit 1). For all antigens, the poorest quintile accounted for the greatest number of deaths averted per million vaccinated. In other words, the benefit of vaccination for a person in the poorest quintile was greater than that for a person in a richer quintile. These findings emphasize the stark differences in health risk and prognostic profiles and access to care across income quintiles. When we compared the poorest two to the richest two quintiles, the ratio of deaths averted per million people vaccinated ranged from 3.7, 3.4, and 3.1 for rubella, human papillomavirus, and *Hemophilus influenzae* type b, respectively, to 1.6 and 1.3 for rotavirus and Japanese encephalitis, respectively (exhibit 3).

### CASES OF MEDICAL IMPOVERISHMENT AVERTED

All vaccines led to an important reduction in the number of cases of medical impoverishment: Overall, an estimated twenty-four million cases of medical impoverishment were averted by vaccines administered in 2016–30 in the forty-one countries. For context as to the magnitude of the impoverishment cases averted, this reduction in the number of cases of medical impoverishment represents approximately 9 percent of the people in low-income countries whose incomes are below the World Bank poverty line of $1.90 a day in 2013.³⁸ The largest number of impoverishment cases averted was attributed to hepatitis B (14 million cases), followed by measles (5 million cases) and *Neisseria meningitidis* serogroup A (3 million cases). The large effect of hepatitis B is due to the combination of high treatment costs and a higher number of future cases averted from the cohorts vaccinated in 2016–30 in the analyzed countries when compared to the other illnesses. For measles, the estimated effect is due to the combination of low treatment costs and hospitalization rates with a high number of

cases. The vaccine for *Neisseria meningitidis* serogroup A would avert cases that have relatively high treatment costs but that are substantially less frequent than cases of measles and hepatitis B. Finally, the vaccines for Japanese encephalitis and rubella would each avert fewer than 150,000 medical impoverishment cases.

The number of medical impoverishment cases decreased greatly with increasing wealth (exhibit 2). As expected, the vast majority of averted impoverishment cases occurred in the poorest quintiles, and fewer than 20,000 cases were averted in the richest quintile. For many vaccines (for example, those for measles, hepatitis B, human papillomavirus, rotavirus, *Neisseria meningitidis* serogroup A, and Japanese encephalitis), more than 40 percent of the averted cases occurred in the poorest quintile. Overall, the results suggest that vaccination would lead to an important reduction in medical impoverishment cases in the poorer quintiles.

Removing the adjustment for total fertility rates would lead to lower ratios of averted deaths

<caption>EXHIBIT 3</caption>

Ratios of deaths averted and of deaths averted per million people vaccinated between the poorest two and richest two quintiles by vaccines to be administered in 41 low- and middle-income countries, 2016–30

<figure><img src="image_3.png" /></figure>

SOURCE Authors' analysis. NOTES The antigens are ordered by total disease burden. The antigens are explained in the notes to exhibit 2.
between the two poorest and two richest quintiles (appendix exhibits A10 and A11).¹⁷ In other words, there would be fewer benefits in the poorest two quintiles than in the base-case results. The poorest quintiles have higher fertility rates (and thus more people susceptible to disease), and removing this adjustment would narrow such differences between the poor and the rich.

## Discussion

This study estimated the future distributional impact of vaccination on mortality and medical impoverishment for ten antigens in forty-one low- and middle-income countries for cohorts vaccinated in the period 2016–30. We found that these vaccine benefits would primarily accrue in the poorest income quintiles. In other words, the projected coverage of vaccine programs would likely relieve more mortality and household economic burden for the poor than for the rich.

It is important to note that the poorest quintiles would gain the most from vaccine programs because they have the most to gain, in both health and economic terms. Differences in health risk and prognostic profiles and care access would likely result in a largely unequal distribution of deaths in the absence of vaccine programs. These estimated differences are so stark that vaccine programs would disproportionately benefit the poor even though that population would have the lowest vaccine coverage. At face value, this suggests that even pro-rich vaccine coverage, in which the richest quintiles enjoy higher coverage rates, would result in the poorest quintiles' gaining greater health outcomes, and that vaccine programs could become even more pro-poor if coverage were equal across income quintiles. Alternatively, we also observe that vaccine programs risk exacerbating existing health inequities if current pro-rich vaccine coverage rates remain.

We acknowledge that in addition to income, there are many other critical equity dimensions worth examining, such as gender and geography—for example, we may see differences in the distribution of benefits between males and females or between rural and urban areas. The distributional analysis presented here could be further applied in the future to examine the distributional impact across these equity-relevant dimensions.

## Policy Implications

Our projection of the distributional impact of vaccines has important policy implications. First, policy makers should be informed about the potentially large health and economic distri-

> Policy makers should view vaccination policies as important channels for improving health equity and reducing poverty.

butional impact that vaccines may have and should view vaccination policies as important channels for improving health equity and reducing poverty. Strong efforts should be made to improve vaccine coverage rates among the poor—for example, by prioritizing the introduction of vaccines in a country's poorer geographic regions, which are likely to be the regions most at risk; and introducing demand-creation activities, such as vaccination campaigns in poorer communities and communication strategies targeting the poor. Further analyses should be conducted to describe local inequities so that context-specific policies could be designed to address those inequities.

Second, distributional impact should be taken into account in decision making about introducing or expanding vaccination programs. Vaccines projected to have greater benefits accruing to the poor (or other marginalized subgroups) could be prioritized over other vaccines or interventions with less equity impact.

Third, when faced with issues of affordability or sustainability of vaccine programs (for example, in countries that will soon “graduate” from receiving aid support), policy makers could consider phased introductions to prioritize the populations most at risk and with the highest projected disease burden.

Fourth, merely ensuring equal access to vaccines will not reduce the health and economic outcome gaps that exist across income quintiles. The poor face higher baseline risks, which are tied to social determinants of health, and they have lower access to treatment. Additional steps may be needed to address those factors.

Finally, and most importantly, empirical data on the distribution of the health (both mortality and morbidity) and financial burden of vaccine-preventable diseases by key subpopulations (such as by income quintile and geographical
setting) are critically needed to enable monitor-
ing of how health policies affect health equity.
Our modeling exercise is a step toward address-
ing this data gap, but we urge governments and
donors to set up data collection systems to gather
this key information.

Conclusion

Vaccines are known to have substantial health
impact and to be cost-effective.²⁰,³⁹ In addition to

highlighting these benefits, this study aimed to
show not only that vaccines could have signifi-
cant health and economic benefits, but also that
these benefits could largely accrue among the
poor. With reducing poverty and improving eq-
uity on the global development agenda, sus-
tained investments in vaccines could make a
large contribution toward achieving the Sustain-
able Development Goals and universal health
coverage. ■

This work was funded by the Bill &
Melinda Gates Foundation (BMGF). The
authors thank Emilia Vynnycky for her
contribution to the development of the
disease impact models and estimates
used in this analysis. Earlier versions of
this article were presented at a Gavi-
BMGF Vaccination Impact Modeling

meeting in Evian, France, in April 2016,
and at the Johns Hopkins Bloomberg
School of Public Health in
October 2016. The authors received
valuable comments from Kate O'Brien,
Peter Hansen, Damian Walker, and Neff
Walker, as well as from two anonymous
reviewers. This is an open access article

distributed in accordance with the terms
of the Creative Commons Attribution
(CC BY 4.0) license, which permits
others to distribute, remix, adapt, and
build upon this work, for commercial
use, provided the original work is
properly cited. See https://
creativecommons.org/licenses/by/4.0/.

NOTES

1 World Bank. Countries back ambitious goal to help end extreme poverty by 2030 [Internet]. Washington (DC): World Bank; 2013 Apr 20 [cited 2017 Dec 29]. Available from: http://www.worldbank.org/en/news/feature/2013/04/20/countries-back-goal-to-end-extreme-poverty-by-2030

2 United Nations. Sustainable Devel-
opment Goals: goal 1: end poverty in
all its forms everywhere [Internet].
New York (NY): UN; [cited 2017 Dec
29]. Available from: http://www
.un.org/sustainabledevelopment/
poverty/

3 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet. 2003;362(9378):111-7.

4 World Health Organization. The world health report 2000: health systems: improving performance [Internet]. Geneva: WHO; 2000 [cited 2017 Dec 29]. Available from: http://www.who.int/whr/2000/en/whr00_en.pdf?ua=1

5 Liu Y, Rao K, Hsiao WC. Medical expenditure and rural impoverishment in China. J Health Popul Nutr. 2003;21(3):216-22.

6 Berman P, Ahuja R, Bhandari L. The impoverishing effect of healthcare payments in India: new methodology and findings. Econ Polit Wkly. 2010; 45(16):65-71.

7 Gupta I. Out-of-pocket expenditures and poverty: estimates from NSS 61st round: draft [Internet]. Delhi: Institute of Economic Growth; 2009 May 12 [cited 2017 Dec 29]. Available from: http://planning commission.nic.in/reports/genrep/indrani.pdf

8 World Health Organization. The

world health report: health systems
financing: the path to universal
coverage [Internet]. Geneva: WHO;
2010 [cited 2017 Dec 29]. Available
from: http://apps.who.int/iris/
bitstream/10665/44371/1/
9789241564021_eng.pdf

9 United Nations. Sustainable Devel-
opment Goals: goal 3: ensure healthy
lives and promote well-being for all
at all ages [Internet]. New York
(NY): UN; [cited 2018 Jan 4].
Available from: http://www.un.org/
sustainabledevelopment/health/

10 Feikin DR, Flannery B, Hamel MJ, Stack M, Hansen P. Vaccines for children in low- and middle-income countries. In: Black RE, Laxminarayan R, Temmerman M, Walker N, editors. Disease control priorities, 3rd ed., vol. 2: reproductive, maternal, newborn, and child health. Washington (DC): World Bank; 2016. p. 187-204.

11 Bloom DE, Canning D. Policy forum: public health. The health and wealth of nations. Science. 2000; 287(5456):1207, 1209.

12 World Health Organization. Global Vaccine Action Plan 2011-2020 [Internet]. Geneva: WHO; 2013 [cited 2017 Dec 29]. Available for download from: http://www.who.int/immunization/global_vaccine_action_plan/GVAP_doc_2011_2020/en/

13 Rheingans R, Atherly D, Anderson J. Distributional impact of rotavirus vaccination in 25 GAVI countries: estimating disparities in benefits and cost-effectiveness. Vaccine. 2012;30(Suppl 1):A15-23.

14 Deogaonkar R, Hutubessy R, van der Putten I, Evers S, Jit M. Systematic review of studies evaluating the broader economic impact of vacci-

nation in low and middle income
countries. BMC Public Health. 2012;
12(1):878.

15 Andre FE, Booy R, Bock HL, Clemens J, Datta SK, John TJ, et al. Vaccination greatly reduces disease, disability, death and inequity worldwide. Bull World Health Organ. 2008;86(2):140-6.

16 Verguet S, Murphy S, Anderson B, Johansson KA, Glass R, Rheingans R. Public finance of rotavirus vaccination in India and Ethiopia: an extended cost-effectiveness analysis. Vaccine. 2013;31(42):4902-10.

17 To access the appendix, click on the Details tab of the article online.

18 Chang AY, Riumallo-Herl C, Salomon JA, Resch SC, Brenzel L, Verguet S. Estimating the distribution of morbidity and mortality of childhood diarrhea, measles, and pneumonia by socio-economic group in low- and middle-income countries. Unpublished paper.

19 US Agency for International Devel-
opment. The DHS Program [Inter-
net]. Rockville (MD): The Program;
[cited 2017 Dec 29]. Available from:
http://dhsprogram.com

20 Lee LA, Franzel L, Atwell J, Datta SD, Friberg IK, Goldie SJ, et al. The estimated mortality impact of vaccinations forecast to be administered during 2011–2020 in 73 countries supported by the GAVI Alliance. Vaccine. 2013;31(Suppl 2):B61-72.

21 Ozawa S, Clark S, Portnoy A, Grewal S, Stack ML, Sinha A, et al. Estimated economic impact of vaccinations in 73 low- and middle-income countries, 2001–2020. Bull World Health Organ. 2017;95(9):629-38.

22 Ozawa S, Clark S, Portnoy A, Stack ML, Grewal S, Sinha A. Methodologies to estimate the health and eco-
nomic impact of vaccination against 10 vaccine-preventable diseases. Baltimore (MD): Johns Hopkins Bloomberg School of Public Health; 2016.

23 Gavi, the Vaccine Alliance. 2016 Strategic Demand Forecast version 12.0. Geneva: Gavi; 2016.

24 Ozawa S, Mirelman A, Stack ML, Walker DG, Levine OS. Cost-effectiveness and economic benefits of vaccines in low- and middle-income countries: a systematic review. Vaccine. 2012;31(1):96-108.

25 Ozawa S, Stack ML, Bishai DM, Mirelman A, Friberg IK, Niessen L, et al. During the “Decade of Vaccines,” the lives of 6.4 million children valued at $231 billion could be saved. Health Aff (Millwood). 2011; 30(6):1010-20.

26 Portnoy A, Ozawa S, Grewal S, Norman BA, Rajgopal J, Gorham KM, et al. Costs of vaccine programs across 94 low- and middle-income countries. Vaccine. 2015; 33(Suppl 1):A99-108.

27 Stack ML, Ozawa S, Bishai DM, Mirelman A, Tam Y, Niessen L, et al. Estimated economic benefits during the “Decade of Vaccines” include treatment savings, gains in labor productivity. Health Aff (Millwood). 2011;30(6):1021-8.

28 World Bank. Out-of-pocket health expenditure (% of total expenditure on health). World Development Indicators [Internet]. Washington (DC): World Bank; c 2017 [cited 2018 Jan 11]. Available from: https://data.worldbank.org/indicator/SH.XPD.OOPC.TO.ZS?view=chart

29 Riumallo-Herl C, Chang AY, Clark S, Constenla D, Clark A, Brenzel L, Verguet S. Poverty reduction and equity benefits from the introduction or scaling up of measles, rotavirus and pneumococcal vaccines in low- and middle-income countries: findings from a modeling study. BMJ Global Health. Forthcoming.

30 World Bank. Gini index (World Bank estimate). World Development Indicators [Internet]. Washington (DC): World Bank; c 2017 [cited 2018 Jan 11]. Available from: https://data.worldbank.org/indicator/SI.POV.GINI?view=chart

31 World Health Organization, World Bank. Tracking universal health coverage: first global monitoring report [Internet]. Geneva: WHO; c 2015 [cited 2017 Dec 29]. Available from: http://www.who.int/healthinfo/universal_healthcoverage/report/2015/en/

32 Pollard SL, Malpica-Llanos T, Friberg IK, Fischer-Walker C, Ashraf S, Walker N. Estimating the herd immunity effect of rotavirus vaccine. Vaccine. 2015;33(32):3795-800.

33 Haber M, Barskey A, Baughman W, Barker L, Whitney CG, Shaw KM, et al. Herd immunity and pneumococcal conjugate vaccine: a quantitative model. Vaccine. 2007;25(29):5390-8.

34 Higgins JPT, Soares-Weiser K, López-López JA, Kakourou A, Chaplin K, Christensen H, et al. Association of BCG, DTP, and measles containing vaccines with childhood mortality: systematic review. BMJ. 2016;355:i5170.

35 Bielicki JA, Achermann R, Berger C. Timing of measles immunization and effective population vaccine coverage. Pediatrics. 2012;130(3):e600-6.

36 Clark A, Sanderson C. Timing of children's vaccinations in 45 low-income and middle-income countries: an analysis of survey data. Lancet. 2009;373(9674):1543-9.

37 Lucero MG, Dulalia VE, Nillos LT, Williams G, Parreño RAN, Nohynek H, et al. Pneumococcal conjugate vaccines for preventing vaccine-type invasive pneumococcal disease and X-ray defined pneumonia in children less than two years of age. Cochrane Database Syst Rev. 2009 Oct 7;(4):CD004977.

38 World Bank. Poverty and shared prosperity 2016: taking on inequality [Internet]. Washington (DC): World Bank; 2016 [cited 2018 Jan 2]. Available from: https://openknowledge.worldbank.org/bitstream/handle/10986/25078/9781464809583.pdf

39 Ozawa S, Clark S, Portnoy A, Grewal S, Brenzel L, Walker DG. Return on investment from childhood immunization in low- and middle-income countries, 2011-20. Health Aff (Millwood). 2016;35(2):199-207.