NCEE Working Paper Preterm Birth and Economic Benefits of Reduced Maternal Exposure to Fine Particulate Matter


NCEE Working Paper

Preterm Birth and Economic
Benefits of Reduced Maternal
Exposure to Fine Particulate Matter

Jina J. Kim, Daniel A. Axelrad, and Chris
Dockins

Working Paper 18-03
May, 2018

U.S. Environmental Protection Agency	NCEE

National Center for Environmental Economics
https://www.epa.qov/environmental-economics

NATIONAL CENTER FOR
ENVIRONMENTAL ECONOMICS

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Preterm Birth and Economic Benefits of
Reduced Maternal Exposure to Fine Particulate Matter

Jina J. Kim, Daniel A. Axelrad, and Chris Dockins

ABSTRACT: Preterm birth (PTB) is a predictor of infant mortality and later-life morbidity.

Despite recent declines, PTB rates remain high in the United States. Growing research suggests
a relationship between a mother's exposure to air pollution and PTB of her baby. Many policy
actions to reduce exposure to common air pollutants require benefit-cost analysis (BCA), and
it's possible that PTB will need to be included in BCA in the future. However, an estimate of the
willingness to pay (WTP) to avoid PTB risk is not available, and a comprehensive alternative
valuation of the health benefits of reducing pollutant-related PTB currently does not exist. This
paper demonstrates a potential approach to assess economic benefits of reducing PTB resulting
from environmental exposures when an estimate of WTP to avoid PTB risk is unavailable. We
utilized a recent meta-analysis and county-level air quality and PTB data to estimate the
potential health and economic benefits of a reduction in air pollution-related PTB, with fine
particulate matter (PM2.5) as our case study pollutant. Using this method, a simulated 10%
decrease from 2008 PM2.5 levels resulted in a reduction of 5,016 PTBs and savings of at least
$339 million, potentially reaching over one billion dollars when considering later-life effects of
PTB.

KEYWORDS: air pollution, preterm birth, benefits, PM2.5
JEL CODES: D61, 118, J13, Q51, Q53

DISCLAIMER

The views expressed in this paper are those of the author(s) and do not necessarily represent those of
the U.S. Environmental Protection Agency (EPA). In addition, although the research described in this
paper may have been funded entirely or in part by the U.S EPA, it has not been subjected to the
Agency's required peer and policy review. No official Agency endorsement should be inferred.

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Preterm Birth and Economic Benefits of
Reduced Maternal Exposure to Fine Particulate Matter**

Jina J. Kim1, Daniel A. Axelrad2, and Chris Dockins2

Introduction

Preterm birth (PTB), or birth before 37 weeks of gestation, is a leading predictor of infant
mortality [1] and an important contributor to later-life disease and disability [2]. Prior analysis
suggests that the relatively high rate of infant mortality [3] in the United States (U.S.) may
largely be due to a high PTB rate, and decreasing the PTB rate could thereby significantly reduce
infant mortality in the U.S. [4]. Research is also increasingly linking PTB to a broad array of
childhood and later-life health outcomes, including neurodevelopmental, respiratory, digestive,
immunological, and cardiovascular problems [2].

A growing body of evidence suggests a relationship between a mother's exposure to
environmental contaminants during pregnancy and PTB of her baby [5-7]. The most extensive
evidence of this relationship is for ambient air pollution. "Criteria air pollutants" are six
pollutants —carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter (PM), and
sulfur dioxide—commonly found across the U.S. for which the Clean Air Act requires the U.S.
Environmental Protection Agency (EPA) to set National Ambient Air Quality Standards (NAAQS).

* This publication was supported by the Cooperative Agreement Number X3-83555301 from the U.S.

Environmental Protection Agency and the Association of Schools and Programs of Public Health. The views
expressed in this publication are those of the authors and do not necessarily represent the official views or policies
of the EPA or ASPPH.

f We thank Neal Fann, U.S. Environmental Protection Agency, for his guidance and assistance with BenMAP, and
Charles Griffiths, U.S. Environmental Protection Agency, for his valuable feedback and review of this paper.

1 Corresponding Author, Association of Schools and Programs of Public Health (ASPPH) Environmental Health
Fellowship Program, Hosted by the National Center for Environmental Economics, Office of Policy, U.S.
Environmental Protection Agency, (202) 566-1898, kim.jina@epa.gov.

2 National Center for Environmental Economics, Office of Policy, U.S. Environmental Protection Agency.

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EPA currently considers existing evidence to be suggestive of a causal relationship between
exposure to five of the six criteria pollutants and reproductive, developmental, and/or birth
outcomes [8-12]. The potential relationship between criteria pollutants and PTB is especially
concerning because a) by nature of the common presence of criteria pollutants, exposure is
often unavoidable; and b) a disproportionate burden of exposure is placed on individuals in
disadvantaged communities, who are already subjected to multiple socioeconomic and health
inequities.

Regulations promulgated under the Clean Air Act to limit or reduce exposure to criteria
pollutants are subject to many requirements by statute, executive order (EO), and EPA policy.
Though benefit-cost analysis (BCA) for setting primary NAAQS is not required by the Clean Air
Act, it has been required for economically significant regulations—those with an annual effect
on the economy of $100 million or more—by a series of executive orders dating back to 1981.
As such, BCA has typically been conducted when setting primary NAAQS and for other
economically significant rulemakings affecting emissions of criteria pollutants or their
precursors.

Estimating human health benefits of reducing any exposure requires health risks to be
quantified and then valued in monetary terms, but data limitations, as well as analytic choices
in risk assessment, often preclude full quantification and valuation. The lack of quantification
for many health outcomes, including adverse birth outcomes such as PTB, poses a challenge for
conducting complete BCAs of reducing harmful environmental exposures. Additionally, the
preferred valuation measure for BCA is willingness to pay (WTP) for risk reduction, defined as
the maximum amount of income one would give up to obtain reduction in risk to one's health.

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In principle, WTP is comprehensive measure that reflects the full set of health outcomes
associated with a reduction in risk, but many health effects, including PTB, lack an estimate of
WTP in the economics literature5. An alternative valuation approach is to focus on the costs
avoided from expected reduced incidence in the population. This requires an estimate of the
direct and indirect costs associated with PTB, such as incremental costs from birth
hospitalization and medical care in infancy, special education, lost wages or productivity, and
later-life health complications [13]. However, few studies exist on the economic costs of PTB,
particularly those considering costs past the neonatal time period. These issues hinder
identifying and adopting the most efficient or cost-effective policies and have been recognized
by the Institute of Medicine6 (IOM), which, in its 2007 report on PTB, recommended
investigation into the economic consequences of PTB in order to better evaluate policies for its
prevention and treatment.

To date, EPA has not included PTB in any BCA. EPA practice for benefits analysis of
criteria pollutant regulations is to consider for inclusion those effects with evidence judged to
be "causal" or "likely causal." EPA's most recent Integrated Science Assessment (ISA) of PM,
published in 2009, reported that the evidence for reproductive and developmental outcomes
overall, including PTB, low birth weight, birth defects, and infant mortality, was suggestive of a
causal relationship. However, the limited studies specifically examining fine particulate matter
(PM2.5) and PTB mostly reported statistically significant positive associations [8]. Newer studies
of PM2.5 and PTB published since 2008 will be considered in an updated ISA that is projected to

5	More details are available in EPA's Guidelines for Preparing Economic Analyses, Chapter 7: Analyzing Benefits
(EPA, 2010).

6	The IOM is now called the Health and Medicine Division of the National Academies of Sciences, Engineering, and
Medicine.

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be completed in 2019 [14]. With the developing evidence for environmental contaminants—
especially air pollution—and PTB, it may be warranted to include PTB in a BCA in coming years.
How this would be done, however, is not immediately apparent, because of the
aforementioned data limitations and complications regarding the many potential health
outcomes also related to PTB.

This study outlines a framework and methodology to examine the potential economic
benefits arising from reducing PTBs resulting from environmental exposures. To illustrate the
process, environmental exposures of interest were first narrowed down to criteria pollutants
because a) there is widespread human exposure to them, indicating high potential benefits of
reducing PTB associated with criteria pollutant exposure; b) with rapid growth of the literature
in recent years, there are now many studies of criteria pollutants and PTB, including meta-
analyses; and c) well-established tools and methods for benefits analysis of these pollutants are
available. We present a case study of maternal exposure to PM2.5 to demonstrate a proposed
approach to estimating the potential health and economic benefits of reducing pollutant-
related PTB.

Methods

Overview. Quantification of PM2.5-attributable PTB reduction and associated economic benefits
entailed the following:

1) Calculation of the reduction in number of PTB cases attributable to a chosen air quality
improvement via decreased ambient PM2.5 levels; and

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2) Valuation (monetization) of immediate and later-life consequences of the PTB cases
derived above.

Primary Analysis: Calculation of Reduced Cases and Immediate Benefits in BenMAP

The Environmental Benefits Mapping and Analysis Program - Community Edition
(BenMAP-CE or BenMAP) is an EPA computer program that quantifies and monetizes the health
impacts of air pollution. BenMAP integrates exposure, population, and health data across the
contiguous U.S.7 and enables translation of a health effect estimate into risk per increment of
exposure. This study utilized BenMAP to estimate the potential PTB benefits of a reduction of
ambient concentrations of county-level PM2.5 nationwide.

The impact of the air quality change on PTB was calculated within BenMAP by specifying
the input factors seen in equation (1), the logistic health impact function used for this study,
where y is the annual reduction in PTBs; y0 is the annual baseline prevalence rate of PTB; 3 is
the coefficient relating PM2.5 and PTB; APM2 5 is the simulated change in PM2.5 concentration;
population is the number of women ages 15 to 44; and fertility rate is the number of live
births per year per woman ages 15 to 44.

y = y 0 ¦

1 -

(1 - y„)' eP APM™ + y0\

¦ population ¦ fertility rate	(1)

7 Because BenMAP does not include data for Alaska or Hawaii, this analysis is for the contiguous U.S. Any mention
of U.S. or national data or analyses in this paper hereafter refers to the contiguous U.S.

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Health impact and valuation results were first calculated at the county level and then
aggregated to provide state-level and national estimates.

Exposure. Daily 24-hour mean PM2.5 measurements reported to the EPA Air Quality System
from ambient air monitoring stations were used to estimate baseline county-level air quality.
BenMAP uses the Voronoi Neighborhood Averaging (VNA) method to interpolate multiple
stationary monitor point values to a county-wide air quality estimate [15]. The VNA method
calculates an inverse-distance weighted average of the monitors surrounding a county's center
to represent the county's overall PM2.5 level8 [16,17]. PM2.5 measurements were taken from
approximately 1,000 monitors in 2008, the most recent year for which EPA provided BenMAP-
compatible air quality data at the time of this study. For this analysis, we simulated a 10%
decrease9 in 2008 annual average county-level PM2.5 concentrations across the country [18].
Population and fertility rate. The population of interest was women in the U.S. ages 15 to 44.
Population data were programmed within BenMAP and originally derived from and predicted
based on U.S. Census data [19]. The Centers for Disease Control and Prevention (CDC) defines
fertility rate as the number of births per woman ages 15 to 44 in a given year [20]. Multiplying
the population of women ages 15 to 44 by fertility rate yielded a unit of all births10, or the
denominator of the prevalence rate. All data were 1) at the county level and 2) from 2008 to
match the most recent BenMAP-compatible air quality data.

8 Predicted estimates tend to be less reliable in rural or remote areas due to fewer monitors being present. These
data inherently represent smaller populations with few to no alternative measurements available, and
measurement error is expected to be negligible for the purposes of this study.

9 Mean ambient PM2.5 across the U.S. decreased by 21.7% from 2008 to 2015, indicating that a simulated 10%
decrease is not an unrealistic air quality improvement.

10 Using this calculation, rather than obtaining separate data on all births specifically, is beneficial for analyses that
may involve future projections of benefits.

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Baseline prevalence rates. The numbers of PTBs and all births in each county were obtained
from CDC WONDER11 for 2008. County-level baseline prevalence rates (y0) were calculated as
all PTBs12 divided by all births in each county. The PTB and all birth values for any counties with
a population of less than 100,000 in a given state were grouped together in CDC WONDER as
"Unidentified Counties" of the state. Any data representing a county with fewer than 10 births
were suppressed in CDC WONDER. To represent rates for unidentified counties or counties with
suppressed data, the statewide rates from the grouped Unidentified Counties were used13.
Health impacts. The 3 coefficient of the health impact function relating PIVh.sand PTB was
derived from a 2015 meta-analysis by Sun et al. [7] of studies measuring the association
between PM2.5 and PTB. Sun et al. included 18 studies conducted mostly in North America,
Europe and Australia, overall totaling over three million study participants. Effect estimates
from each study were extracted and converted to regression coefficients per 10 |-ig/m3 increase
in PM2.5 to obtain a pooled estimate. The authors reported results for PTB as pooled odds ratios
(ORs) per 10 |-ig/m3 increase in PM2.sfor varying exposure periods, exposure assessment
methods, and study types. Thirteen of the aforementioned 18 studies included exposure data
for the entire pregnancy. The pooled OR for maternal exposure to PM2.5 during the entire
pregnancy, derived from these 13 studies, was 1.13 (95% confidence interval = 1.03, 1.24). We
converted the central estimate of this pooled OR to a logistic regression 3 coefficient of 0.012

11 Wide-ranging Online Data for Epidemiologic Research

12 CDC WONDER has data for continuous gestational age, but the binary variable was used in this study in
accordance with the prevailing PM2.5 epidemiological literature available.

13 Unidentified Counties rates rather than statewide rates were used in counties with suppressed data under the
assumption that unidentified counties would be more similar to counties with suppressed data, as both have
smaller, less dense populations. Any possible measurement error from this assumption is expected to be minimal,
as these data inherently represent much smaller populations.

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relating risk per 1 |ag/m3 increase in PM2.5. by using the general formula ln(OR) = (S ¦ APM2s
as illustrated in the BenMAP User Manual Appendices [21].

Economic valuation. The monetized benefits of the reduction in PTB resulting from the
simulated air quality improvement were calculated within BenMAP, which applies a given
valuation function to the cases of PTB calculated by the health impact function. Ideally, the
analysis would employ a WTP value for reduced risk of PTB that would account not just for
medical costs and lost productivity, but for all or most of the expected consequences associated
with PTB, including long-term health consequences and any intangible effects on quality of life.
However, no such estimates exist in the economics literature. A second-best valuation strategy,
which we adopt here, is to first estimate the immediate or early-life cost of illness (COI)
associated with PTB and then to add the present value of costs associated with longer-term
consequences.

For our primary analysis, we draw upon the lOM's report on PTB which included a COI
estimate representing an average over all PTBs in 2005 dollars with costs after the first year of
life discounted at a 3 percent rate. The report estimated costs for several consequences of PTB;
for each of these consequences, the estimate represents the average cost of each PTB
incremental to the average cost of a term birth. The COI included all incremental medical care
costs from birth to age 5 years; incremental maternal delivery costs; early intervention costs, or
costs of targeted services for children from birth to age 3 who have developmental delays or
other delay-related health conditions; and medical care, special education, and individual lost
productivity costs for the following four developmental disabilities (DDs), experienced by a
subset of individuals born preterm and averaged over all PTBs, for ages 6 and older: cerebral

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palsy, intellectual disability (mental retardation), vision impairment, and hearing loss. These
values are described in Table 1. The cost estimate for each category was converted to 2014
dollars within BenMAP.

Table 1. Summary of PTB costs as derived from IOM report.

Cost Categories

Average Incremental

Index used to update IOM

Costs per PTB (2005$)

estimate to 2014$

Medical Costs associated with:

$37,022

Medical Costs

• Maternal Delivery

• Birth to Age 5 Years

• Cerebral palsy, intellectual

disability, vision impairment,

and hearing loss (4 DDs)

Early Intervention

$3,353

All Goods

Special Education (4 DDs)

Lost Productivity (4 DDs)

$11,214

Wages

Total

$51,589

It is important to note that this PTB COI estimate does not account for several significant
cost categories, such as costs after age 5 outside of those for the four aforementioned DDs or
lost productivity costs for the parents of the person born preterm, thereby underestimating the
value of reduced PTB [2]. For a more complete estimate of the value of reducing PTB, some
additional PTB-related costs were estimated, as detailed in the next section. Furthermore,
although the estimates from the IOM report have been widely used in the literature, the report
also includes recommendations for refined analyses that would improve the accuracy of their
estimates. These recommended improvements include undertaking multivariate modeling to
better understand the large variance in economic burden across the population and performing
analyses of the effects of race, ethnicity, and/or socioeconomic status on this burden.

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Supplemental Analysis: Additional Benefits of Reduced PTB

Additional later-life outcomes of PTB were assessed for availability of adequate data on 1)
evidence of their association with PTB, and 2) the WTP to reduce or avoid the later-life outcome
or the COI of the outcome. Little or no information was found quantifying WTP or COI for most
post-neonatal health outcomes, effects on familial dynamics, or earnings and education in the
U.S. outside those already quantified by the IOM. However, the available data for intelligence
quotient (IQ) deficits, asthma, and diabetes mellitus (types 1 and 2) included meta-analyses of
their relationship with PTB, and thus were deemed adequate for the analysis. Benefits
calculations were performed at the national level to provide a broad overview of these
potential benefits. All values are present values discounted at 3 percent and are expressed in
2014 dollars.

Cognitive benefits: IQ. Kerr-Wilson et al. 2012 [22] conducted a meta-analysis of the
relationship between PTB defined as both a binary variable (preterm vs. term) and a categorical
variable (extremely, very, and moderately preterm, or <28, 28-31, and >32 weeks vs. term) and
IQ deficits. The meta-analysis included 27 studies of 7,044 children total. The average
gestational age of the pre-term subjects in many of the studies was lower than that of PTBs in
the U.S. overall. Because babies born preterm are on average moderately preterm—i.e. fewer
babies are born at increasingly lower gestational ages—the moderately preterm category was
used rather than the binary preterm category. Moderately preterm babies had a weighted
mean IQ score 8.4 (6.6, 10.2) points lower than that of term babies.

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EPA has routinely valued the benefits of avoided IQ decrements14 based on the effect of
IQ on lifetime earnings, as was done to estimate the cognitive benefits of reduced exposure to
lead and methylmercury. In the most recent application [23] of this model, EPA derived average
lifetime earnings values from U.S. Census data and used Salkever et al. 1995's [24] estimates15
[25, 26] to calculate an economic cost of $15,884 for each IQ point loss.

Asthma. Sonnenschein-van der Voort et al. 2014 [27] evaluated the relationship between PTB
and school-age asthma defined as "asthma diagnosis reported between 5 and 10 years (no,
yes)," preferably physician diagnosed, across 18 studies of European16 cohorts. The meta-
analysis reported a pooled OR of 1.40 (1.18, 1.67). This OR and the prevalence of PTB and
asthma [28] were used to estimate the number of asthma cases among PTBs.

Blomquist, Dickie, and O'Conor 2011 used data from two surveys to estimate annual
WTP for asthma control for selected ages of children ranging from 4-17. To account for children
between ages 4 and 17, the applicable survey elicited parents' values of controlling their
children's asthma. The survey reported WTP estimates for ages 4, 5, 8, 11, 15, and 17, and a
linear interpolation between these values was used to value intervening years. These values
were used to approximate the present value at birth of WTP for diagnosis of asthma at "school-

14 The present IQ valuation is not expected to overlap with costs of reduced work productivity previously included
in the IOM estimates, which account for labor market participation rates, disability-specific work limitation, and
earnings losses associated with limitations. The potential for slight overlap with regards to the 4 DD's is recognized;
however, they are relevant to a small portion of total PTBs, and any possible overlap is expected to be negligible.

15 EPA also uses Schwartz 1994 estimates, which yield a cost per IQ point of $11,559. However, Salkever 1995 was
re-examined in 2014 and was deemed to be better suited for the present analysis.

16 It was assumed that incorporating studies of European populations in the present analysis would not produce
appreciable problems arising from non-generalizability, as the health relationships of interest were not believed to
be related largely to cultural or national differences.

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age" by discounting the stream of annual WTP estimates from ages 4-17 back to age zero using
a discount rate of 3%. The estimated net present value was $38,541 per case.

Diabetes mellitus. Li et al. 2014 conducted a meta-analysis of PTB and both type 1 and type 2
diabetes mellitus (T1D and T2D, respectively) separately. A total of 18 studies for T1D were
from the U.S., Canada, Europe, and Australia. The total five T2D studies include four studies
from Europe (UK, Sweden, Finland, Denmark) and one from China, with various methods of
outcome ascertainment ranging from self-report to physician diagnosis. Although for T2D there
is uncertainty arising from the aforementioned traits of the study, this meta-analysis was still
the most appropriate available at the time of the present study, and was deemed acceptable
for use in the exploratory nature of this study. PTB was significantly associated with both T1D
(OR = 1.18 (1.11, 1.25)) and T2D (OR = 1.51 (1.32,1.72)). The respective ORs and prevalence of
PTB, T1D, and T2D [29] were used to estimate the number of cases of each diabetes type.

The American Diabetes Association (ADA) estimated annual costs per case of diabetes
(type unspecified) of $8,298 in direct medical costs and $3,224 in reduced productivity costs17.
Because approximately 95% of diabetes cases are T2D and approximately 5% are T1D, the cost
estimates from the ADA were assumed to largely represent T2D costs and were therefore used
to calculate benefits of reducing T2D cases. To derive an estimate of lifetime costs from the
ADA annual costs estimates, we assumed onset of T2D at age 50, death at age 8018, and

17 Reduced productivity costs were assumed to be additive to those calculated previously in this study, as those
estimates were based on 1) the four DD's previously mentioned in the IOM report, and 2) IQ-related productivity.
Costs of increased mortality from diabetes were only included in the form of productivity loss.

18 This is a simplifying assumption but generally consistent with conditional life expectancy at age 50

(httpsi//www.ssa.gov/oact/STATS/tabIe4c6.htm 1). Lost workplace productivity costs are only included up to age
65.httpsi//www,ssa,gov/oact/STATS/table4c6,html).

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discounted the resulting stream of costs back to birth at 3 percent. The estimated net present
value was $48,508 per case.

Tao et al. 2010 estimated expected lifetime medical costs and income loss from T1D in
the U.S. by categories of age of onset from ages 3 to 45. To calculate present values, we
assumed costs were uniformly distributed within the specified age categories (e.g., from 3-9
years old) and then discounted these age-specific costs to age zero. Summing these values
across all ages of onset resulted in a net present value of $199,313 of lifetime costs per case of
T1D.

Results

Primary Analysis Results: Immediate Benefits

In 2008, there were 432,677 PTBs and 4,203,437 total births in the contiguous U.S., translating
to a PTB rate of 0.103 (Table 2). The air quality data used for the baseline scenario, or before
any simulated air quality change, indicated a nationwide range of county-level PM2.5 of 4.60 to
18.62 |-ig/m3, with a mean of 10.02 |-ig/m3 and median of 10.45 |-ig/m3 (Figure 1). The change in
air quality from the simulated 10% decrease in county-level PM2.5 ranged from 0.46 to 1.86
l-ig/m3 across the states (Figure 2).

Table 2. Baseline scenario of preterm birth rates in the contiguous U.S. with no reduction in
ambient PM2.5 in 2008.



All U.S.

Baseline PTBs (n)

432,677

Baseline All Births (n)

4,203,437

PTB Rate

0.103

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Figure 1. Distribution of baseline county-level PM2.5 annual mean concentrations in the U.S.
(2008)

		Minimum = 4.60

—	Mean =10.02
		Median = 10.45

—	Maximum = 18.62

5	10	15

Baseline annual mean of PM25 (ug/m3)

Figure 2. Changes in county-level PM2.5 levels (ng/m3) after a simulated 10% decrease from
baseline 2008 levels.

A hypothetical 10% reduction from baseline 2008 county-level PM2.5 levels was estimated to
result in 5,016 fewer PTBs (1.16 of PTBs) for a total of $339 million of benefits nationwide
(Table 3). The majority of benefits were from medical costs, which constituted about $251
million of the $339 million of benefits overall in the primary analysis. Numbers of reduced cases

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and associated benefits varied by state, with the percentage of PTB cases reduced from the
simulated PM2.5 reduction ranging from 0.6 to 1.4% of the state's PTBs overall (Table 4).

Table 3. National changes in preterm birth cases and associated economic benefits after a

simulated 10% decrease in PlVk.sfrom baseline 2008 levels (2014$).

Reduced PTB Cases (n)

5016

Benefits from Reduced PTB (2014$ millions)

$339.1

Special Education Costs

$20.4

Medical Costs

$250.7

Lost Wages

$68.1

Table 4. State-level changes in preterm birth cases and associated economic benefits after a

simulated 10% decrease in PlVk.sfrom baseline 2008 levels.

State

Baseline PTB
Cases(n)

Reduced PTB
Cases(n)

PTB Case
Reduction (%)

Benefits from
Reduced PTB
(2014$ millions)

Alabama

8,263

102

1.2%

$6.9

Arizona

10,038

117

1.2%

$7.9

Arkansas

4,705

56

1.2%

$3.8

California

48,992

620

1.3%

$41.9

Colorado

6,679

51

0.8%

$3.5

Connecticut

4,056

47

1.2%

$3.2

Delaware

1,212

16

1.3%

$1.1

District of Columbia

1,090

14

1.2%

$0.9

Florida

25,623

211

0.8%

$14.2

Georgia

16,987

213

1.3%

$14.4

Idaho

2,342

20

0.8%

$1.3

Illinois

18,229

235

1.3%

$15.9

Indiana

9,369

125

1.3%

$8.4

Iowa

3,906

43

1.1%

$2.9

Kansas

3,845

40

1.0%

$2.7

Kentucky

6,832

88

1.3%

$5.9

Louisiana

8,163

85

1.0%

$5.8

Maine

1,176

10

0.9%

$0.7

Maryland

8,399

107

1.3%

$7.2

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Massachusetts

6,694

72

1.1%

$4.9

Michigan

12,680

148

1.2%

$10.0

Minnesota

6,343

66

1.0%

$4.5

Mississippi

6,082

71

1.2%

$4.8

Missouri

8,283

98

1.2%

$6.6

Montana

1,240

10

0.8%

$0.7

Nebraska

2,574

24

0.9%

$1.6

Nevada

4,310

43

1.0%

$2.9

New Hampshire

1,142

11

1.0%

$0.7

New Jersey

11,779

142

1.2%

$9.6

New Mexico

2,933

19

0.6%

$1.3

New York

23,906

280

1.2%

$18.9

North Carolina

13,984

172

1.2%

$11.7

North Dakota

875

7

0.8%

$0.5

Ohio

15,871

217

1.4%

$14.6

Oklahoma

6,026

70

1.2%

$4.7

Oregon

3,847

37

1.0%

$2.5

Pennsylvania

15,126

202

1.3%

$13.6

Rhode Island

1,186

12

1.0%

$0.8

South Carolina

7,405

89

1.2%

$6.0

South Dakota

1,037

9

0.9%

$0.6

Tennessee

9,743

117

1.2%

$7.9

Texas

45,246

508

1.1%

$34.4

Utah

5,387

54

1.0%

$3.6

Vermont

529

5

0.9%

$0.3

Virginia

11,151

135

1.2%

$9.1

Washington

7,940

80

1.0%

$5.4

West Virginia

2,540

34

1.3%

$2.3

Wisconsin

6,091

79

1.3%

$5.3

Wyoming

821

6

0.7%

$0.4

U.S. Range

529-48,992

5-620

0.6-1.4%

$0.3-41.9

Supplemental Analysis Results: Additional Benefits

The previously calculated 5,016 PTBs was carried through to calculate the additional potential
economic benefits from avoiding IQ decrements, asthma, T1D, and T2D cases. For this

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simulation, the greatest category of benefits was by far from the avoided IQ point decrements,
which yielded an estimated $669 million (Table 5).

Table 5. Additional benefits from avoided later-life health outcomes of preterm birth after a
simulated 10% decrease in PlVk.sfrom baseline 2008 levels.

n (IQ points or Cases)

Benefits per n
(2014$)

Total Benefits Estimation
(2014$ millions)

42,134 IQ points

$15,884

$669.3

Asthma

160 cases

$35,272

$5.6

Type 1 Diabetes

4 cases

$199,313

$0.8

Type 2 Diabetes

190 cases

$48,508

$9.2

Discussion

PTB is an important health outcome for which epidemiological studies are increasingly finding
associations with environmental contaminants. Estimates of the effects of a policy or risk
management action on the incidence of PTB and the value of this change in incidence could be
used to better inform decision-making. In this study, we explored an approach to quantifying
the economic benefits of avoiding PTB and applied it to a hypothetical reduction in PM2.5. We
found that the potential annual PTB benefits from reducing PM2.5 in our primary analysis may
be in the order of hundreds of millions of dollars, possibly rising to over a billion dollars when
also considering additional later-life health outcomes. For perspective, on a per-case-avoided
basis, the value of PTB (including later-life health outcomes) is greater than for other non-fatal
PM2.5 health effects generally considered in EPA analyses except for chronic bronchitis [30].
EPA's most recent assessment of PM, published in 2009, determined that the evidence for
PM2.5 and reproductive and developmental outcomes, a category that included PTB, was
suggestive of a causal association. The epidemiologic literature on this topic is much more

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extensive now than when the previous assessment was completed, and it is conceivable that
the new PM2.5 assessment scheduled for completion in 2019 could determine that the weight of
evidence is sufficient to conclude a likely causal or causal relationship between PM2.5 and PTB. If
so, PTB would become a strong candidate for inclusion in future analyses of the benefits of
PM2.5 reductions. However, even if the PM2.5 evidence concerning PTB is not judged to rise to a
likely causal or causal weight-of-evidence determination, the analysis presented in this paper of
the benefits of reduced PTB will be applicable to any other environmental contaminants that
may be found to have sufficient evidence. In either case, this type of benefits calculation19
would prove to be especially useful, as there is no existing WTP value for PTB, and the COI
estimate in the IOM report, while useful, is dated and incomplete.

We used a two-step procedure to estimate the secondary outcomes reported in this
study (IQ, asthma, T1D, and T2D), in which the first step was to compute the number of cases of
PTB avoided, and the second step was to apply quantitative relationships from the literature
regarding health consequences of PTB. The most recent PM ISA did not investigate the
relationship between prenatal PM2.5 exposure and the secondary outcomes reported in this
study. If there were direct evidence of a possible relationship of prenatal PM2.5 with IQ, asthma,
T1D, or T2D, that evidence would be a primary consideration in a decision whether to include
these secondary outcomes in a PM2.5 benefits analysis. In the absence of such direct evidence, it
is reasonable to assume that the health consequences of PTB indicated in the literature are

19 In theory, benefits could be estimated for changes in gestational age if 1) the PM2.5 epidemiological literature
provided adequate effect size estimates for gestational age as a continuous variable, and 2) sufficient evidence of
causality was found in the weight-of-evidence determination of the relationship between PM2.5 and continuous
gestational age.

19

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outcomes that would be avoided with any reduction in PTBs that results from lowered exposure
to PM2.5.

We estimated that 5,016 PTBs would have been avoided in 2008 with a 10% reduction
in PM2.5, resulting in $339 million of immediate benefits and over $669 million of additional
health benefits. We have fairly high confidence in the estimate of PTBs avoided, conditional on
the assumption that increased PM2.5 exposure increases the risk of PTB. The meta-analysis from
which the PTB beta coefficient was derived, Sun et al. 2015, was the most comprehensive meta-
analysis available at the time of our current study, and integrates estimates from many studies
conducted in geographically diverse populations with a majority of studies from the U.S. The OR
was 1.13 with a 95% confidence interval of 1.03 to 1.24, indicating to us with relatively strong
confidence that there is a moderate and significant effect of PM2.5 on PTB. However, there
remains uncertainty regarding the exact nature and magnitude of the PM2.5-PTB relationship,
such as effects potentially varying by phase of gestation. For example, Sun et al. 2015 indicated
statistically significant heterogeneity among studies, which subgroup and sensitivity analyses
revealed to be due in part, but not entirely, to exposure assessment, study design, and study
settings. Additionally, the literature regarding trimester-specific effects or predictive power
remains mixed [31-35]. The pooled estimate used in this study was from 13 studies of whole-
pregnancy exposure, for which there were the greatest number of studies available and
therefore the most statistical power. The effect estimates from the first, second, and third
trimesters separately were almost identical, but fewer studies examined trimester-specific data
(ten, five, and nine studies respectively), and all trimester-specific estimates were statistically

-------
insignificant. Thus, Sun et al. 2015 did not allow us to draw any strong conclusions regarding
possible trimester-specific differences.

Another source of uncertainty in our analysis comes from the limited availability of
comprehensive health and costs data. In our primary analysis, we used the lOM's estimates of
the costs of PTB, which included limited medical costs, early intervention and special education
costs, and lost wages, and adjusted these costs to 2014 dollars to derive a valuation estimate.
However, the lOM's estimates did not include many later-life health, earnings, or education
costs. For the purposes of our supplemental analysis, health and cost data were insufficient or
not available for most potential later-life outcomes. We searched the literature to identify later-
life outcomes associated with PTB and found many outcomes that had been studied, but most,
such as cardiovascular disease or autism spectrum disorder, were not included in our analysis
for one or more of the following reasons: 1) Low birth weight (LBW) was used as a proxy
outcome for PTB in many earlier epidemiological studies. Evidence increasingly suggests that
LBW and PTB, while overlapping, also have distinct etiologies and effects [2]. Therefore, we did
not consider it appropriate to include studies conflating the two outcomes; 2) For many
potential health outcomes of interest, evidence was not considered sufficient for
quantification—there were no meta-analyses available to use for estimating incidence, only a
few studies, mixed results, and/or results were statistically insignificant; 3) Some outcomes only
had sufficient data for developing countries, which were assumed to differ greatly from the
U.S., especially with regard to health care systems and economic outcomes; 4) For some
outcomes, the outcome definition differed between the health data and valuation data; and 5)
many outcomes simply lacked valuation estimates in the economics literature, even if they had

21

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epidemiologic evidence suitable for quantification. Among the outcomes we did include and
value (IQ, asthma, T1D, and T2D), the body of literature regarding their relationship with PTB
and their costs was not extremely comprehensive; additional research in these areas is
expected to improve these estimates. The most robustly valued outcome was IQ, for which
there could be uncertainty regarding the cost estimates used from Salkever, which have been
debated in the literature [26, 36-39]. However, based on methodological choices—for example,
other studies did not consider work participation rates, demographic changes, or more recent
data—Salkever's IQ-earnings estimates were deemed most appropriate for this study. Cost
estimates for IQ were based on earnings, which are likely to underestimate WTP. However,
among the four outcomes that were valued, IQ was still the dominant driver of costs; costs for
the other three outcomes (asthma, T1D, and T2D) were relatively small.

Finally, there is uncertainty regarding the estimate used for the quantitative relationship
between PTB and IQ. The population of infants in the Kerr-Wilson et al. 2012 study used to
quantify this relationship was heavily skewed toward very or extremely preterm babies. As
mentioned in the Methods section, because babies born preterm are on average moderately
preterm (rather than very or extremely preterm), the moderately preterm category in Kerr-
Wilson et al. 2012 was used over the binary preterm category to reduce possible
overestimation of benefits that could result from including the higher costs associated with
very preterm babies. The moderately preterm estimate compares mean IQfor births at
gestational ages of 34 to 36 weeks to mean IQ for births at gestational ages of 37 weeks and
greater. Depending on how our estimated decrease in PTB affects the overall distribution of
PTB, using the moderately preterm category could still be an overestimate. For the PTBs

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avoided from a reduction in PM2.5, if we can assume that a child at any point of the preterm
distribution can enter any point of the term distribution, then a comparison of mean-to-mean
costs for moderately preterm (which constitutes most preterm babies) and term babies is
generally correct, and use of Kerr-Wilson et al. 2012's moderately preterm estimate should be
accurate. However, if the simulated decrease in PTB results in a small but overall shift in the
distribution of gestational ages, i.e. those right below the cutoff for term birth (very close to but
not quite meeting 37 weeks) cross the somewhat arbitrary boundary for term birth (37 weeks
and greater), then the average shift in gestational age may be much smaller than the shift
underlying the Kerr-Wilson et al. 2012 estimate. This effect may occur because of differences by
continuous gestational age within not only the moderately preterm category, but also the term
category (e.g. outcomes may differ between babies born at 37 versus 40 weeks) [40].
Regardless, no alternate value with less uncertainty in these respects was available, and we
found utilizing the moderately preterm category from Kerr-Wilson to be a reasonable estimate
given the current state of knowledge.

Our study is, to the best of our knowledge, the first study to simulate a decrease in
PM2.5 and subsequent decrease in PM2.5-related PTB, and to then quantify the PTB-related
economic benefits arising from the simulated reduction in PM2.5. Trasande et al. 2016
estimated the economic costs of all PTBs attributable to anthropogenic PM2.5 exposure in 2010

[41]. PM2.5 was assumed to be anthropogenic, rather than arising from natural sources such as
wildfires, dust storms, or volcanoes, at levels above 8.8 |-ig/m3, a reference level which was
originally applied in the 2010 Global Burden of Disease estimates of PM2.5-attributable disease

[42]. The OR of 1.15 (1.14, 1.16) from Sapkota et al. 2012 [34], a meta-analysis which included 6

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studies of the relationship between PM2.5 and PTB, was utilized in the calculation of PM2.5-
attributable PTB cases; conversely, we used the Sun et al. 2015 meta-analysis, which reports a
slightly lower OR, as it was more recent and included many more studies. Medical costs from
birth to age 5 and costs after age 5 for developmental disabilities were obtained from the 2007
IOM report also used in the present study, and lost economic productivity was measured
through IQ loss. Kerr-Wilson et al. 2012 was also used in Trasande et al. 2016; however,
Trasande et al. used the 11.9-point IQ decrement between term babies and preterm babies on
average in the study, whereas we used the 8.4-point IQ decrement between term babies and
moderately preterm babies for reasons stated above. Trasande et al. did not include other
later-life outcomes, such as those that we evaluated in our study (asthma, T1D, and T2D).
Additionally, rather than using the Salkever estimates as we did, Trasande et al. used the
estimates of changes in earnings per IQ point from Grosse et al. 2002, which are lower than the
estimates proposed by Salkever 2014 [36]. They estimated 15,808 PTBs attributable to PM2.5 in
2010, with nationwide costs of $5.09 billion (2010$) for medical care costs and lost economic
productivity combined. Although the Trasande et al. study was different in that it quantified
economic costs of all PM2.5-attributable PTBs, while our study quantified costs for a fixed,
simulated decrease in PM2.5 and PTB, the two are consistent in indicating a high economic
burden of PTB in the U.S.

Our study is also the first to utilize BenMAP to assess effects of prenatal exposures.
BenMAP is used by EPA to perform benefits analyses of reduction of criteria pollutant emissions
and subsequent changes in incidence of health outcomes. Previously, its use was limited to
health impacts on directly exposed populations; the capacity of BenMAP to evaluate health

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impacts of prenatal exposures provides potential for its use in a broader range of future
benefits analyses.

A principal benefit of this analytical approach is that it provides a straightforward way to
estimate benefits in the absence of an existing WTP estimate for reduced risk of PTB. Because
this method does not rely on an overarching WTP estimate for reduced PTB, the growing
scientific knowledge base and new literature on specific PTB-related health outcomes can
quickly be incorporated into calculations, allowing for direct revisions of benefits calculations
based on the prevailing science. These qualities allow researchers and policymakers to obtain a
broad overview of the health benefits of adverse environmental exposure reductions in a timely
manner.

The literature on environmental contaminants and birth outcomes is robust and
growing [5, 43-50]. Stieb et al. 2012, which analyzed multiple air pollution and birth outcomes
in 62 studies by pollutant, outcome, and exposure period20, already provides a foundation for
potential future analyses for ozone, NO2, or SO2, which can be performed through BenMAP and
for which BenMAP-compatible measurements can be obtained [6]. In addition, specific health
outcomes, such as high blood pressure [51], have suggested or established relationships with
PTB, and may therefore hold promise for future valuation estimates. This type of study could be
undertaken to quantify economic effects of pollutant-related health outcomes currently
unquantified in BCAs, which can contribute to more comprehensive analytical underpinnings of
future decision-making.

20 Sun et al. 2015 was used in the analysis over Stieb et al. 2012 because the latter included only six studies
measuring the PM2.5-PTB association.

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Conclusions

Although PTB is an important health outcome with both short- and long-term consequences
that may yield significant economic costs, there is not a robust body of economic literature to
support an estimate of the WTP to reduce its risk. There is a need to develop methodologies
and estimates that can provide information regarding the potential benefits of reducing such
detrimental health outcomes. The analysis presented here of PTB and PM2.5 indicate that
previously unquantified benefits of reducing pollution-related cases of health outcomes may be
substantial and are worthy of investment for future research.

26

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