1

Causes of Death and Infant Mortality Rates among Full Term Births in the United

States: An Analysis of 10,175,481 Children Born Full-term in the U.S. between 2010

and 2012

Authors: Neha Bairoliya1), Ph.D., Günther Fink2), Ph.D.

Author affiliation:

1) Harvard Center for Population and Development Studies, 9 Bow St

Cambridge, MA 02138. Email: nbairoli@hsph.harvard.edu

2) Swiss Tropical and Public Health Institute and University of Basel, Switzerland.

Corresponding Author

Neha Bairoliya

Harvard Center for Population and Development Studies

9 Bow St

Cambridge, MA 02138

Ph: 612-817-7379

Email:nbairoli@hsph.harvard.edu

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Abstract

Background: While the high prevalence of preterm births and its impact on infant mortality in the U.S.

has been widely acknowledged, recent data suggests that even full-term births in the U.S. face substan-

tially higher mortality risks compared to leading European countries. In this paper, we use the most re-

cent birth records in the U.S. to more closely analyze the primary causes of mortality rates among full-

term births.

Methods and Findings: Linked birth and death records for the period 2010-2012 were used to identify

state- and cause specific burden of infant mortality among full-term infants (37-42 weeks of gestation).

Multi-variable logistic models were used to assess the extent to which state-level differences in full term

infant mortality (FTIM) are attributable to observed differences in maternal and birth characteristics.

Random effect models were used to assess the relative contribution of state-level variation to FTIM.

Hypothetical mortality outcomes were computed under the assumption that all territories could achieve

survival rates of the best-performing states. A total of 10,175,481 children born full-term in the U.S. be-

tween January 1, 2010 and December 31, 2012 were analyzed. FTIMR was 2·2 per 1000 live births

overall, and ranged between 1.29 (95 % CI [1.08,1.53]) and 3.77 (95 % CI [3.39,4.19]) at the state level.

Zero states reached the rates reported in leading European countries (FTIMR < 1·25), and 13 states had

FTIMR > 2·75. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital

malformations and perinatal conditions accounted for 31% and 11·3% of FTIM, respectively. Largest

mortality differentials between states with good and states with poor FTIMR were found for sudden un-

expected deaths of children (SUDI), with particularly large risk differentials for deaths due to SIDS

(2.52 OR, 95% CI [2.29,2.77]) and suffocation (3.99 OR, 95% CI [3.31,4.83]). Even though a substan-

tial proportion of these differences was explained by state-level differences in maternal education and

maternal health, state of residence was predictive of child survival in fully adjusted models. The extent

to which these state differentials are due to differential antenatal care standards as well as differential

access to health services could not be determined due to data limitations. Overall, our estimates suggest

that infant mortality could be reduced by 4,003deaths (95% CI [2284,5587]) annually if all states were

to achieve the mortality levels of the best performing state in each cause-of-death category.

Conclusions: More than 7000 full-term infants die in the U.S. each year. The results presented in this

paper suggest that a substantial share of these deaths may be preventable. Potential improvements seem

particularly large for SUDI, where very low rates have been achieved in a few states while average mor-

tality rates remain high in most other areas.

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Introduction

Despite some progress made in the recent years, infant mortality rates in the U.S. continue to be high

compared to other developed countries [1]. According to the latest estimates, the U.S. currently ranks

44th among 199 high and low income countries, with an infant mortality rate of 5·6 deaths per 1000 live

births in 2015, about three times the rate observed for countries at the very top of the ranking [1].

While a large literature has highlighted the high rates of prematurity and prematurity-related mortality in

the U.S. [2, 3], the U.S. actually does quite well when it comes to the survival of preterm infants. Fig 1

compares gestation-specific mortality rates in the U.S. and six leading European countries with data

available for 2010. On average, infant mortality appears to be very similar for premature births in the

U.S. and in leading European countries. The same is not true for children born after 36 weeks of gesta-

tion, where children born in the U.S. face more than twice the mortality risk of children in leading Euro-

pean countries (OR 2.02, 95% CI [1.84, 2.22]). A recent CDC report suggests that these mortality gaps

among full-term births now account for almost 50% of infant mortality gap between Sweden and the

United States [4].

[Fig 1 here]

In this paper, we use the complete and geocoded birth records from 2010-2012 to better understand the

high burden of mortality among full-term infants in the United States. We identify the main causes un-

derlying the high mortality rates among full-term children overall in the aggregate data in a first step,

and then explore differences in actual and potential birth outcomes across U.S. states in a second step.

By first reviewing the causes of death in this population, we can identify the main risk factors for chil-

dren in this generally low-risk population, and clearly distinguish the relative importance of pre-existing

conditions such as malformations relative to perinatal and post-neonatal conditions. In order to provide a

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better sense of feasible outcomes in this population, we estimate and compare cause-specific full-term

mortality rates at the state level both unconditional and conditional on maternal characteristics. While

these state-level comparisons do not allow us to identify the specific reasons why states have particular-

ly high rates of mortality, they do allow us to identify areas where major improvements are possible in

principle.

Methods

Study Design

The study was designed as a cross-sectional study using birth and death records of all children born in

the U.S. between January 1st, 2010 and December 31st, 2012. No pre-analysis plan was developed for

this study. The main objective of the project was to identify the primary causes underlying the high in-

fant mortality rates observed in the U.S. nationally as well as at the state level.

Data Sources

Linked birth and death records including restricted geographic identifiers were obtained from the Na-

tional Center for Health Statistics (NCHS) for the years 2010 to 2012. All children in the birth and

death records can be directly linked to the geographic identifiers in these data sets (100% match rate).

Additional data for background Fig 1 was downloaded from the Euro-Peristat webpage at

http://www.europeristat.com/our-indicators/euro-peristat-perinatal-health-indicators-2010.html.

Outcome Measures

Our primary outcome measure of interest was infant mortality rate among full-term births defined as the

number of deaths per 1000 children born alive between 37 and 42 weeks of gestation. For the purpose of

this study, we use the traditional definition of full-term, which includes early term (37 & 38 weeks), full

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term (39 & 40 weeks), late term (41 weeks) and some post-term (42 weeks) births according to the more

recent definition of the American College of Obstetricians and Gynecologists Committee on Obstetric

Practice [5] . To adjust for differential outcomes in this relatively wide five week gestational window,

we control for differences in gestational age by including binary indicators for gestational length catego-

ry (37 weeks, 38 weeks, 41 weeks, 42 weeks) in our multivariable analysis, using the more narrow re-

vised full term definition (39 and 40 weeks) as our reference group. Gestational length was computed by

the NCHS based on last menstrual period reported by the mother. To ensure gestational length was not

measured differentially across states, we compared prematurity rates with rates of low birth weight in

the full sample at the state level; the correlation of these measures at the state level was 0.97; the strong

alignment between birth weight and reported gestational length is also further supported by the descrip-

tive statistics provided in the supplemental materials.

Causes of death for all deceased children were based on death certificates, which had to be completed

either by a coroner or medical examiner in all U.S. territories following CDC guidelines. Even though

regulations vary by state, deaths due to violence or suspicious circumstances are further investigated and

certified by a medical legal officer [6].

Death certificates were reviewed and coded following ICD-10 guidelines by the NCHS. For the purpose

of this paper, we grouped reported causes of death into 4 main categories:

1) Congenital malformations: ICD-10 codes Q00-Q99

2) Sudden unexpected death of infants (SUDI): ICD10 codes V01-Y89 and R00-R99

3) Perinatal conditions: ICD-10 codes P00-P96

4) All other causes: all other ICD-10 codes.

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The SUDI grouping was chosen intentionally to minimize potential state-level differences in the attribu-

tion of unexplained deaths to SIDS vs. “other unexplained causes” [7-9].Some more disaggregated sta-

tistics for major causes of deaths (such as SIDS) were also computed as described further below.

Exclusion Criteria

Children born prior to 37 or after 42 weeks of gestation were excluded from this study. All other chil-

dren born alive in the US between 2010 and 2012, including multiple births and children born with mal-

formations (which were not reported in the data set), were analyzed in this study.

Covariates

In order to assess the extent to which state-level differences can be attributed to differences in maternal

characteristics and birth outcomes, we considered the following variables included in the original data

file: mother’s age, educational attainment, smoking behavior, diabetes, chronic hypertension and ec-

lampsia. We divided maternal age into five categories (age < 20, 20-34, 35-39, 40-44 and age >44) and

used ages 20-34 as reference group in our multivariable analysis. Similarly, we divided maternal educa-

tional attainment into four categories: < high school, high school or some college credit without degree,

associate or bachelor’s degree, master's degree or doctorate. As for smoking, mothers report the average

number of cigarettes smoked by the mother during her first, second and third trimesters. From this we

construct indicators for smoking or not (number of cigarettes > 0) for each trimester. We use indicators

for previous diagnosis of diabetes, chronic hypertension and eclampsia as provided in the data. All these

variables are based on mother’s self-report in the hospital around the time of delivery and are reported

on the birth certificate. In addition, we include controls for birth weight category (< 1500, 1500-1999,

2000-2499,2500-2999, 3000-3499, 3500-3999, 4000-4499 and >4499), multiple births (1 if singleton, 2

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if twin, 3 if triplet, 4 if quadruplet and 5 if quintuplet or higher), infant gender and gestational length

(indicators for 37 weeks, 38 weeks, 41 weeks, 42 weeks of gestation) in our empirical models. Further

details of all these variables are provided in the Supplemental Table S1.

Statistical Methods

As a first step, we computed full-term infant mortality rates (FTIMR) at the state level, and classified all

U.S. territories into five categories: areas with excellent survival rates (FTIMR < 1·25 – the European

benchmark shown in Figure 1), areas with good survival rates (1·25 <=FTIMR < 1·75), areas with aver-

age survival (1·75 <=FTIMR < 2·25), areas with fair survival rates (2·25 <=FTIMR < 2·75) and finally

areas with poor survival rates (FTIMR >= 2·75). The “excellent” category was chosen based on the rates

observed in the six European countries, which ranged between 0.97 and 1.24, with a median FTIMR of

1.11. The remaining categories were defined by sequentially adding 0.5 deaths per 1000 term birth (a

50% increase relative to the European average) to the cutoffs. In a second step, we decomposed mortali-

ty differences at the group level by cause of death. Third, we used multi-variable regression models to

assess the extent to which survival differences across states can be attributed to observable differences in

maternal and child characteristics. To do so, we first ran multivariable logistic models comparing infants

born in the states with the highest mortality rates to infants born in states with the lowest mortality rates.

We estimated three separate models: a first model, where we did not adjust for any covariates; a second

model, where we adjusted for maternal characteristics outlined in the covariates section above; and a

third model (proposed by a reviewer), where we adjusted for maternal characteristics and birth outcomes

(gestational lengths, infant gender, birth weight and multiple births). Model 2 was estimated to assess

the extent to which state level differences can be attributed to local variation in maternal characteristics

such as age, education and health status. Model 3 was estimated to assess the extent to which subsequent

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mortality differentials were explained by local variation in the prevalence of multiple births as well as

differences in birth weight and the distribution of gestational length. In all three models, each observa-

tion corresponds to a child born full-term in the sample period. To assess the overall contribution of state

level characteristics to variation in FTIMR, we also estimated multi-level logistic models where we

nested individual observations within states, and then estimated between state variance in unconditional

as well as in models conditioning on maternal and birth characteristics. Lastly, we computed hypothet-

ical mortality rates (which we refer to as “counterfactuals”) under the assumptions that i) all U.S. territo-

ries would achieve mortality levels in the top group and ii) that all U.S. territories would achieve the

mortality rates of state with the lowest mortality rate in each cause of death category.

Results

A total 10,175,481 children born full-term in the U.S. between January 1, 2010 and December 31, 2012

were analyzed. Average FTIMR was 2·19 [2·16, 2·22] per 1000 full time live birth in the pooled sam-

ple. At the state level, estimated FTIMR ranged between 1·29 [1·08, 1·53] in Connecticut and 3·77

[3·39, 4·19] in Missouri. No state or territory was classified as “excellent”; 10 states including Connect-

icut were classified as good, 17 as average, 11 as fair, and 13 as poor (see Supplemental Figure S1 and

Supplemental Table S2 for details).

Fig 2 compares early (death in the first 6 days after birth), late (death between 7 and 27 days after birth),

and post-neonatal (death in 28-364 days after birth) mortality rates across mortality groups. While only

relatively minor differences were found with respect to early neonatal mortality, large absolute and rela-

tive differences were found for the post-neonatal periods; with an average of 9·5 [9·1,9·9] deaths per

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10,000 full-term births in states classified as “good”, and a mortality rate of 20·9 [20·1, 21·6] death per

10,000 in the lowest ranking territories.

[Figure 2 here]

Fig 3 summarizes the main causes of full term infant mortality (FTIM). Sudden unexpected death in in-

fancy (SUDI) accounted for the largest proportion of deaths overall (43%), followed by congenital mal-

formations (31%) and perinatal conditions (11·3%). The mortality risk due to congenital malformations

increased from 5·6 deaths per 10,000 full-term live births in states with FTIMR < 2·75 to 8·4 deaths in

the bottom group. The risk of SUDI increased from 5·6 in the states classified as “good” to 15·4 in the

states classified as “poor”. Observed absolute mortality differences were smallest for perinatal condi-

tions, with an estimated mortality rate of 2·1 in the best performing group, and an estimated mortality of

2·8 in the bottom group.

[Figure 3 here]

In Supplemental Figures S2 and S3, we provide further details on the primary causes of congenital mal-

formations. The two most common causes of deaths due to congenital malformation were Edwards Syn-

drome and congenital malformations of the heart, which accounted for 10·9 and 14·6 percent of congen-

ital malformation deaths, respectively.

We find that 42·2% of SUDI were due to SIDS (ICD10: R95), followed by unknown and ill-defined

causes (ICD10: R99), which accounted for 20·6% of SUDI, and accidental suffocation and strangulation

(ICD10: W75), which accounted for 16·1% of SUDI. Supplemental Figures S4-S8 provide further de-

tails on the spatial distribution of cause-specific deaths.

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Supplemental Figure S9 summarizes the relative importance of the four mortality groups in the neonatal

and post-neonatal periods. Congenital malformations account for 58·1 and 43·3% of overall mortality in

the early and late neonatal periods, respectively. Perinatal conditions account for 31·7% and 22·8% of

mortality in the same periods. In the post-neonatal period – that accounts for the majority of deaths

overall (63·5% as shown in Fig 2), the large majority (60%) of deaths are due to SUDI.

Table 1 shows estimated relative risks for the bottom (“poor”) group compared to the top group of states

(“good”) for the four main infant mortality categories displayed in Figure 3. In the first column, we

show unadjusted odds ratios (OR). In the second column, we show OR estimates adjusted for a full set

of covariates summarized in Supplemental Table S1. In unadjusted models, living in a state with high

FTIMR was associated with an increased odds of FTIM due to perinatal conditions of 36% (OR 1·36,

95% CI [1·196, 1·535]) as well as increased odds of death due to congenital malformations by 51%

(1·514, [1·406, 1·630]). Risk differentials were largest for SUDI, with an estimated odds ratio of 2·75

[2·578, 2·929]). When we adjust for maternal age, education and measures of health status, estimated

risk differentials decline for all risk factors, with largest declines for SUDI, where estimated ORs fall

from 2·75 in unadjusted models to 1.81 in models adjusting for both maternal and birth characteristics.

In general, differences between models 2 (adjusting for maternal characteristics only) and 3 (adjusting

for maternal characteristics and birth conditions) were small and not statistically different.

[Table 1 here]

In Table 2, we show estimated state variability in mortality outcomes based on multi-level logistic mod-

els. State level variation was highest for SUDI (estimated state-level variance 0.118 [0.068,0.168]) and

congenital malformations (0.061, [0.028,0.095]). These state-level differences were reduced substantial-

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ly for all causes when we controlled for differences in maternal characteristics, with particularly large

reductions for SUDI, where estimated state variability dropped to 0.040 [0.018,0.063] when both mater-

nal and birth condition controls were included in the model.

[Table 2 here]

Table 3 shows estimated annual FTIM for our two hypothetical scenarios. Under the assumption that all

states would achieve the survival outcomes of the 10 states with the lowest mortality outcomes overall,

infant mortality would decline by an estimated 2023 [1717, 2329] deaths each year. Under the more am-

bitious counterfactual that all states could achieve the cause-specific mortality rates of the best-

performing state in each category, the infant mortality among full term birth would be reduced by 4003

deaths [2284, 5587] each year. Under both hypothetical scenarios, only about 10% of the potential im-

provements are related to perinatal conditions or other causes. More than 75% of the excess burden of

mortality in both scenarios is due to congenital malformations and SUDI.

[Table 3 here]

Discussion

The results presented in this paper paint a rather dark picture of the survival probabilities of full-term

infants in the U.S. compared to leading European countries. Pooling all available data between 2010 and

2012, we find that no single U.S. state or territory achieves the survival rates currently reported in lead-

ing European countries, with children born full-term in the 10 best-performing states facing about 50%

higher risks of infant mortality, and children born in bottom quintile of U.S. states facing almost three

times the infant mortality risk of leading European countries.

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Given that survival rates among preterm infants in the U.S. are generally below comparable rates in the

same European countries (as illustrated in Figure 1), clinical care during or immediately after delivery is

likely not the main issue in the U.S. In the sample analyzed, perinatal conditions – where health care

quality likely matters most – account only for about 11% of total infant mortality among full-term births.

In terms of the big picture, the high burden of FTIM in the U.S. seems to be mostly due to SUDI and

congenital malformations, which account for 42·9% and 31·1% of the total infant mortality burden

among full-term children, respectively, and for almost 80% of excess deaths in our counterfactual analy-

sis. From a policy perspective, deaths due to malformations are quite different from deaths classified as

SUDI. Malformations are in practice hard, if not impossible, to prevent; in most cases, the only way to

“prevent” malformation-related infant mortality is to increase screening and early termination, which

does affect fetal survival overall, but may at least lower the emotional and physical burden for parents

and may also prevent substantial health systems cost. In terms of the overall magnitude of these effects,

we find malformation-specific FTIMR of less than 3 per 10,000 live births in states like Vermont and

New Jersey, and rates three times higher in quite a few states in the Mississippi delta and surrounding

states (see Supplemental Figure S3 for details). Globally, the WHO estimates that 330,000 children die

annually during the neonatal period due to congenital malformations [10, 11], which corresponds to a

risk of approximately 2·5 deaths per 10,000. Taking these global estimates as a (not very ambitious)

benchmark suggests that the children in the U.S. face about three times the risk of death due to malfor-

mation in other countries; more research comparing cause-specific mortality rates with other countries

will be critical to identify differences in the prevalence of genetic defects as well as the most effective

screening interventions to allow early detection.

With respect to actual health improvements, the area with the most obvious and ample room for increas-

ing the chances of child survival is sudden unexpected deaths of infants. Given that the attribution of

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deaths to SIDS versus “other unexplained causes” is not obvious in many cases [7, 8], we mostly fo-

cused on the larger “unexpected infant deaths: (SUDI) category in this paper. More than 3000 infants die

in the U.S. each year due to causes which are – as the name suggests – not expected and really should

not happen for the most part. This is maybe most immediately obvious when it comes to accidental suf-

focation or strangulation in bed; it is both remarkable and sad that over 600 infants die in the U.S. each

year due to suffocation which appears relatively easy to prevent with appropriate sleeping arrangements.

SUDI mortality in the best-performing states of the U.S. (California and New York) are below 6 per

10,000; rates remain more than twice as high (>12) in twelve states, including Ohio, South Dakota and

Tennessee. A large fraction of these deaths are due to SIDS, to which previous studies have attributed

6·4 deaths per 10,000 births [12]. Our results suggests SIDS incidence rates as low as 1·27 and 1·32 per

10,000 full-term live birth in Nevada and New Mexico and as high as 13·33 and 8·75 in states Arkansas

and Mississippi. A large proportion of these state-level differences in both SIDS and the broader SUDI

category appear to be attributable to state-level differences in maternal age and maternal education. As

shown in the more detailed regression results in Supplemental Materials Table S4, maternal characteris-

tics are indeed remarkably predictive of these outcomes: compared to children born to mothers with in-

complete high school education, children of highly educated mothers have 77% lower odds of SUDI

mortality. The finding that the risk of SUDI mortality appears to also almost linearly decline with ma-

ternal age (conditional on all other factors) suggests that these outcomes are strongly influenced by ma-

ternal behavior and the early home environments, both of which should at least in principle be modifia-

ble through targeted information and behavioral change interventions.

Our analysis is not without limitations. First, we have relatively little information on children’s home

environments, and thus cannot directly identify what is happening at children’s home or compare under-

lying risk factors. Second, it is possible that state level estimates that we present may be biased if people

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move despite the fact that 97% of respondents indicate that the state of birth is also their state of resi-

dence. Third, as mentioned above, we do not have information on abortion rates at the state level, which

are likely to (at least partially) explain differences in birth outcome observed. Last, it seems likely that

some of the less common causes of deaths (particularly in the ICD10 R and W categories) are miscoded

or coded differentially across states. To reduce this type of measurement error, we group all SUDI

deaths together for most of our analysis.

Conclusion

More than 7000 children born alive at full term in the United States each year die within their first year

of life. The results presented in this paper suggest that a substantial proportion of these deaths are pre-

ventable, with particularly large improvements possible for sudden unexpected deaths for children.

Acknowledgments: The authors would like to thank the National Center for Health Statistics as well as

Euro-Peristat for the data provided to this project.

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Fig 1: Relative Mortality Risk in the U.S. and Europe by Gestational Length Category

Figure 1 shows infant mortality risk in the U.S. compared to the average rates observed in Austria, Denmark, Finland, Norway, Sweden and Switzerland.

Sources: Euro-Peristat, U.S. birth and death records, author calculations. Mortality rates are computed among children born at 37 or more weeks of gestation.

Gestational length in both the Euro-Peristat and U.S. data is based on the best obstetrical estimate available, which in most cases corresponds to first trimester

ultrasound.

17

Fig 2: Group-Specific Mortality by Age of Death

Figure 2 shows the number of infant deaths per 10,000 full-term infants in the U.S. by period and overall mortality group for the years 2010 to 2012 as well as

the percentage of deaths in each category. Early neonatal mortality is defined as death in the first 6 days after birth. Late neonatal mortality is defined as deaths

between 7 and 27 days after birth and post neonatal mortality is defined as death 28 to 364 days after birth.

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Fig 3: Cause-Specific Mortality Rates

Figure 3 shows total number of deaths in each category by FTIMR group for the years 2010-2012 as well as the percentage of deaths of each group in the respec-

tive categories. The following ICD-10 causes of death were included in each group: Congenital malformations: Q00-Q99; SUDI: V01-Y89 and R00-R99, Peri-

natal conditions: P00-P96; Other: all other causes.

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Table 1: Relative Odds of Cause-specific Full-term Infant Mortality in States with Poor FTIMR Relative to States with Good

FTIMR

Unadjusted Odds Adjusted Odds

(I) (II)

Odds

Ratio 95% CI Odds Ratio 95% CI Odds Ratio 95% CI

Congenital malfor-

mations 1.514*** (1.238,1.851) 1.441*** (1.286,1.615) 1.378*** (1.152,1.648)

Perinatal conditions 1.355*** (1.175,1.562) 1.219* (1.022,1.452) 1.175 (0.984,1.403)

SUDI 2.748*** (2.460,3.070) 1.839*** (1.606,2.107) 1.808*** (1.589,2.057)

Other causes 1.581*** (1.370,1.825) 1.430*** (1.180,1.733) 1.404*** (1.169,1.685)

Good states

Total births 2,885,191

Total deaths 4,589

Poor states

Total births 1,476,604

Total deaths 4,551

Notes: Table shows relative odds of cause specific mortality in states with overall poor mortality indicators compared to states

classified as "good". The states classified as "good" include CA, CT, HI, MA, MD, NH, NJ, NV, NY and VT and the states classi-

fied as "poor" include AL, AR, DE, KY, LA, ME, MS, OH, OK, SD, TN, WY, WV. Column (I) adjusts for maternal characteris-

tics including mother’s age, education, smoking behavior and health conditions like diabetes, chronic hypertension and eclampsia.

Column (II) adjust for maternal characteristics and birth outcomes including gestational length, infant gender, birth weight and plu-

rality.

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Table 2: Variation (on Logit Scale) in Cause-Specific Mortality Between U.S. States Estimated using the Random Intercept

Logistic Model

Congenital malfor-

mations Perinatal conditions SUDI Other causes

No controls

0.061*** (0.028,0.095)

0.021* (0.002,0.039)

0.118*** (0.068,0.168)

0.033** (0.012,0.054)

Maternal controls

0.033** (0.011,0.055)

0.013 (0.004,0.028)

0.042*** (0.019,0.066)

0.031* (0.006,0.056)

Maternal and Birth out-

come controls 0.039** (0.015,0.064) 0.014 (0.004,0.030) 0.040*** (0.018,0.063) 0.029* (0.005,0.053)

Notes: Estimates show state level variation in mortality outcomes. Variances as well as 95% confidence intervals estimated using multivariable

logistic model, where individuals (level 1) are nested into states (level 2). The results of the fully specified model are displayed in Supplemental

Materials Table S4.

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Table 3: Estimated Preventable Deaths among Full-term Births

Actual

number of

deaths

Counterfactual Scenario

Mortality of top group Best U.S. territory

2010-2012 Predicted

[95% CI] Deaths

prevented Predicted

[95% CI] Deaths

prevented

Congenital malfor-

mations 2,308 1897 [1805,1990] 411 683 [0,1456] 1,625

Perinatal conditions 839 711 [654,767] 128 340 [0,724] 499

SUDI 3,187 1906 [1813,1998] 1,281 1752 [1458,2046] 1,435

Others 1,098 894 [831,958] 203 654 [387,921] 444

Total full-term deaths 7,431 5408 [5102,5714] 2,023 3428 [1844,5147] 4,003

Notes: Based on an estimated 3·4 Million full term live births per year. The best territory estimates are from

Vermont (Congenital Malformations, 2·01 deaths per 10,000 full-term births [0, 4·28]), Rhode Island (Perina-

tal conditions, 1·00 death per 10,000 full-term births [0, 2·13]), New Jersey (SUDI, 5·15 deaths per 10,000

full-term births [4·29, 6·02]) and Oregon (Others, 1·92 deaths per 10,000 full-term births [1·14, 2·71]).

22

Supplemental material

Supplemental Figure S1: State-level FTIMR Classification

Supplemental Figure S2: Primary Causes of Deaths due to Malformation

Supplemental Figure S3: Spatial Distribution of Full-Term Infant Mortality due to Malformations

Supplemental Figure S4: Primary Causes of Deaths due to SUDI

Supplemental Figure S5: Infant Deaths per 10,000 Full-Term Live Births Classified as Sudden Unex-

pected Death of Infants (SUDI)

Supplemental Figure S6: Infant Deaths per 10,000 Full-Term Live Births due to Violence or Assault

Supplemental Figure S7: Infant Deaths per 10,000 Full-Term Live Births due to Suffocation

Supplemental Figure S8: Deaths per 10,000 Full-Term Live Births Across States due to SIDS

Supplemental Figure S9: Percentage of Deaths in Early, Late and Post Neonatal Periods due to Specific

Causes

Supplemental Table S1: Sample Characteristics

Supplemental Table S2: State-specific Mortality Rates

Supplemental Table S3: Cause-specific Mortality Distribution by Mortality Groups

Supplemental Table S4: Estimates from Random Intercept Logistic Model (Odds Ratio)

Supplemental Table S5: Estimates from a Logistic Model using Mortality Groups (Odds Ratio)

23

Supplemental Figure S1: State-level FTIMR Classification

Legend: Figure shows state level classification: areas with good survival rates (1.25<=FTIMR < 1.75), areas with average

survival (1.75<=FTIMR < 2.25), areas with fair survival rates (2.25<=FTIMR < 2.75) and areas with poor survival rates

(FTIMR>2.75). All estimates are for full-term infants born in 2010-2012.

24

Supplemental Figure S2: Primary Causes of Deaths due to Malformation

Legend: Figure shows the FTIMR burden for the 7 most common causes of death due to malformation by mortality group

among full-term infants born in 2010-2012.

25

Supplemental Figure S3: Spatial Distribution of Full-Term Infant Mortality due to Malformations

Legend: Figure shows number of neonatal deaths per 10,000 full-term infants due to malformations among full-term infants

born in 2010-2012.

26

Supplemental Figure S4: Primary Causes of Deaths due to SUDI

Legend: Figure shows the FTIMR burden for the most common causes of death classified as sudden unexpected death of

infants (SUDI) by mortality group among full-term infants born in 2010-2012.

27

Supplemental Figure S5: Infant Deaths per 10,000 Full-Term Live Births Classified as Sudden

Unexpected Death of Infants (SUDI)

Legend: Figure shows number of neonatal deaths per 10,000 full term infants born in 2010-2012. Estimates include all deaths

filed under ICD-10 codes V01-Y89 and R00-R99.

28

Supplemental Figure S6: Infant Deaths per 10,000 Full-Term Live Births due to Violence or As-

sault

Legend: Figure shows number of neonatal deaths per 10,000 full term infants born in 2010-2012. Estimates include all deaths

filed under ICD-10 codes Y079 (Unspecified perpetrator of maltreatment and neglect) and Y09 (assault by unspecified

means).

29

Supplemental Figure S7: Infant Deaths per 10,000 Full-Term Live Births due to Suffocation

Legend: Figures shows number of deaths per 10,000 full term infants in 2010-2012 due to suffocation. Estimates include all

deaths filed under ICD codes W75 (accidental suffocation and strangulation in bed) or W84 (unspecified threat to breathing).

30

Supplemental Figure S8: Deaths per 10,000 Full-Term Live Births Across States due to SIDS

Legend: Figure shows number of deaths per 10,000 full term infants in 2010-2012 due to SIDS. Estimates include all deaths

filed under ICD code R95 (Sudden infant death syndrome).

31

Supplemental Figure S9: Percentage of Deaths in Early, Late and Post Neonatal Periods due to

Specific Causes.

Legend: Figure shows the percentage of deaths occurring due to each cause of death in the early neonatal (1), late neonatal

(2) and post-neonatal periods (3). Early neonatal mortality is defined as death in the first 6 days after birth. Late neonatal

mortality is defined as deaths 431 between 7 and 27 days after birth and post neonatal mortality is defined as death 28 to 364

days after birth.

32

Supplemental Table S1: Sample Characteristics

Good Average Fair Poor Total

Number of full-term live

births 2,885,191 3,575,864 2,237,822 1,476,604 10,175,481

Cause specific deaths (N)

SUDI 1617 3217 2454 2272 9560

Congenital malfor-

mations 1610 2418 1649 1247 6924

Perinatal conditions 603 877 618 418 2516

Others 759 1127 793 614 3293

Total full-term infant

deaths 4589 7639 5514 4551 22293

Mothers characteristics

Age Group (N %)

Age < 20

188847 (6.55

%)

296208 (8.28

%)

199432 (8.91

%)

155323

(10.52 %)

839810

(8.25 %)

Age 20-34

2147292

(74.42 %)

2778098

(77.69 %)

1759575

(78.63 %)

1175067

(79.58 %)

7860032

(77.24 %)

Age 35-39

435038

(15.08 %)

407686

(11.40 %)

227826

(10.18 %)

120575 (8.17

%)

1191125

(11.71 %)

Age 40-44

106687 (3.70

%)

88652

(2.48 %)

48275

(2.16 %)

24306

(1.65 %)

267920

(2.63 %)

Age > 44

7327

(0.25 %)

5220

(0.15 %)

2714

(0.12 %)

1333

(0.09 %)

16594

(0.16 %)

Education (N %)

< High School

456663

(15.83 %)

555858

(15.54 %)

310531

(13.88 %)

170715

(11.56 %)

1493767

(14.68 %)

High School/College

Credit

1544982

(53.55 %)

1859683

(52.01 %)

1219180

(54.48 %)

965841

(65.41 %)

5589686

(54.93 %)

Associate/Bachelor's

Degree

587772

(20.37 %)

864037

(24.16 %)

518811

(23.18 %)

249925

(16.93 %)

2220545

(21.82 %)

Master's De-

gree/Doctorate

295774

(10.25 %)

296286 (8.29

%)

189300 (8.46

%)

90123

(6.10 %)

871483

(8.56 %)

Mothers Health (N %)

Diabetes (any diagnosis)

5614679

(194.83 %)

6940071

(194.74 %)

4330212

(194.85 %)

2865695

(194.84 %)

1.98e+07

(194.81 %)

Chronic Hypertension

(any diagnosis)

5735594

(199.03 %)

7087902

(198.89 %)

4414997

(198.67 %)

2914888

(198.18 %)

2.02e+07

(198.78 %)

Eclampsia

(any diagnosis)

5758952

(199.84 %)

7122404

(199.86 %)

4440687

(199.83 %)

2938002

(199.75 %)

2.03e+07

(199.83 %)

33

Good Average Fair Poor Total

Any smoking in 1st tri-

mester

95114

(3.99 %)

204656

(6.86 %)

185320

(13.35 %)

158085

(15.67 %)

643175

(8.28 %)

Any smoking in 2nd tri-

mester

75994

(3.19 %)

175658

(5.89 %)

158419

(11.42 %)

139239

(13.80 %)

549310

(7.08 %)

Any smoking in 3rd tri-

mester

71880

(3.01 %)

167033

(5.60 %)

152669

(11.01 %)

133935

(13.28 %)

525517

(6.77 %)

Gestational age (N %)

37 weeks

268651

(9.31 %)

374730

(10.48 %)

228877

(10.23 %)

170211

(11.53 %)

1042469

(10.24 %)

38 weeks

550827

(19.09 %)

731615

(20.46 %)

441048

(19.71 %)

321455

(21.77 %)

2044945

(20.10 %)

39 weeks

955096

(33.10 %)

1215495

(33.99 %)

774934

(34.63 %)

520323

(35.24 %)

3465848

(34.06 %)

40 weeks

705100

(24.44 %)

793865

(22.20 %)

506116

(22.62 %)

296847

(20.10 %)

2301928

(22.62 %)

41 weeks

315990

(10.95 %)

343941

(9.62 %)

217561

(9.72 %)

121217

(8.21 %)

998709

(9.81 %)

42 weeks

89527

(3.10 %)

116218

(3.25 %)

69286

(3.10 %)

46551

(3.15 %)

321582

(3.16 %)

Infant gender = male

1469493

(50.93 %)

1820740

(50.92 %)

1139987

(50.94 %)

752855

(50.99 %)

5183075

(50.94 %)

Birth weight (N %)

< 1500 grams

1270

(0.04 %)

1942

(0.05 %)

1300

(0.06 %)

1067

(0.07 %)

5579

(0.05 %)

1500-1999 grams

6908

(0.24 %)

9469

(0.26 %)

6317

(0.28 %)

4967

(0.34 %)

27661

(0.27 %)

2000-2499 grams

74135

(2.57 %)

98088

(2.74 %)

65636

(2.93 %)

48547

(3.29 %)

286406

(2.81 %)

2500-2999 grams

489769

(16.98 %)

617289

(17.26 %)

388163

(17.35 %)

279371

(18.92 %)

1774592

(17.44 %)

3000-3499 grams

1207793

(41.86 %)

1504589

(42.08 %)

927314

(41.44 %)

617248

(41.80 %)

4256944

(41.84 %)

3500-3999 grams

851038

(29.50 %)

1042500

(29.15 %)

654229

(29.24 %)

409746

(27.75 %)

2957513

(29.07 %)

4000-4499 grams

220155

(7.63 %)

260800

(7.29 %)

168325

(7.52 %)

100354

(6.80 %)

749634

(7.37 %)

> 4499 grams

34123

(1.18 %)

41187

(1.15 %)

26538

(1.19 %)

15304

(1.04 %)

117152

(1.15 %)

Plurality (N %)

Single

2832295

(98.17 %)

3520225

(98.44 %)

2201579

(98.38 %)

1454701

(98.52 %)

1.00e+07

(98.36 %)

Twin

52615

(1.82 %)

55283

(1.55 %)

36018

(1.61 %)

21792

(1.48 %)

165708

(1.63 %)

34

Good Average Fair Poor Total

Triplet

276

(0.01 %)

333

(0.01 %)

199

(0.01 %)

103

(0.01 %)

911

(0.01 %)

Quadruplet

5

(0.00 %)

23

(0.00 %)

17

(0.00 %)

7

(0.00 %)

52

(0.00 %)

Quintuplet or higher

0

(0.00 %)

0

(0.00 %)

9

(0.00 %)

1

(0.00 %)

10

(0.00 %)

35

Supplemental Table S2: State-specific Mortality Rates per 10,000 Full-term Live Births

U.S. states

Full term infant

mortality due to

congenital malfor-

mations

Full term infant

mortality due to

perinatal condi-

tions

Full term infant

mortality due to

SUDI

Full term infant

mortality due to

other causes

AK 4.08 1.7 10.88 4.76

AL 8.48 3.32 15.41 4.03

AR 9.63 2.43 17.46 4.02

AZ 8.34 2.39 8.3 3.52

CA 6.64 2.02 5.37 2.65

CO 7.1 2.68 8.03 2.68

CT 3.17 1.78 5.45 2.48

DC 9.46 3.15 6.02 3.15

DE 7.25 4.14 9.67 7.25

FL 6.08 2.25 10.00 3.17

GA 5.95 2.61 11.4 3.11

HI 2.76 4.25 5.74 2.98

IA 5.87 2.02 9.31 3.34

ID 6.32 3.08 8.89 3.25

IL 5.55 2.17 9.31 3.69

IN 8.29 3.48 11.08 4.53

KS 6 2.06 12.01 3.47

KY 8.11 1.95 14.19 4.28

LA 7.52 2.22 15.43 6.34

MA 4.68 1.63 6.05 2.42

MD 4.06 2.95 7.28 3.00

ME 7.06 2.45 14.73 3.38

MI 6.61 2.90 10.77 3.08

MN 6.86 2.25 6.98 3.6

MO 9.08 3.08 11.69 3.44

MS 9.4 4.00 19.88 4.43

MT 4.55 2.92 16.25 2.27

NC 8.2 2.33 10.73 3.9

ND 4.23 2.47 16.21 2.47

NE 9.34 1.93 7.86 2.67

NH 2.62 3.2 5.83 2.04

NJ 3.71 1.82 5.15 2.46

NM 7.36 2.65 7.8 3.38

NV 5.76 1.73 6.56 2.65

NY 5.64 2.14 5.48 2.6

OH 9.03 2.89 13.1 4.02

OK 8.54 2.33 17.15 3.26

OR 6.1 3.51 9.78 1.92

PA 7.77 2.96 9.89 3.67

RI 4.33 1.00 9.66 3.00

SC 6.44 2.85 13.75 3.66

36

U.S. states

Full term infant

mortality due to

congenital malfor-

mations

Full term infant

mortality due to

perinatal condi-

tions

Full term infant

mortality due to

SUDI

Full term infant

mortality due to

other causes

SD 7.78 4.36 12.45 4.67

TN 8.33 2.56 17.17 3.16

TX 7.47 2.56 8.96 2.92

UT 8.09 3.06 7.8 2.62

VA 7.36 2.45 9.15 3.39

VT 2.01 1.34 8.7 4.02

WA 6.54 2.52 9.19 2.74

WI 5.72 2.23 9.27 4.35

WV 7.92 4.34 15.64 3.2

WY 3.86 3.86 15.43 4.41 Notes: Based on the pooled sample covering all full-term infants born in the United States between 2010

and 2012. The following ICD-10 causes of death were included in each group: Congenital malformations:

Q00-Q99; SUDI: V01-Y89 and R00-R99, Perinatal conditions: P00-P96; Other: all other causes.

37

Supplemental Table S3: Cause-specific Mortality Distribution by Mortality Groups

FTIMR

Group

All cause

full term

infant mor-

tality

Full term infant

mortality due to

congenital mal-

formations

Full term

infant mor-

tality due to

perinatal

conditions

Full term

infant mor-

tality due to

SUDI

Full term

infant mor-

tality due to

other causes

Good

Median 1.67 0.56 0.20 0.54 0.27

IQR 0.08 0.20 0.01 0.01 0.00

Minimum 1.29 0.20 0.13 0.52 0.20

Maximum 1.73 0.66 0.43 0.87 0.40

Average

Median 2.15 0.69 0.25 0.92 0.32

IQR 0.09 0.14 0.03 0.04 0.05

Minimum 1.80 0.41 0.10 0.60 0.19

Maximum 2.24 0.95 0.35 1.09 0.48

Fair

Median 2.43 0.78 0.29 1.08 0.35

IQR 0.29 0.19 0.06 0.15 0.06

Minimum 2.25 0.42 0.21 0.83 0.23

Maximum 2.74 0.91 0.35 1.62 0.45

Poor

Median 3.12 0.85 0.26 1.54 0.40

IQR 0.22 0.09 0.06 0.41 0.10

Minimum 2.75 0.39 0.20 0.97 0.32

Maximum 3.77 0.96 0.44 1.99 0.73

Total

Median 2.16 0.66 0.24 0.92 0.31

IQR 0.76 0.18 0.07 0.47 0.10

Minimum 1.29 0.20 0.10 0.52 0.19

Maximum 3.77 0.96 0.44 1.99 0.73

Table shows state-level cause-specific mortality rates by FTIMR group. All estimates correspond to infant

deaths per 1000 full-term live births. Numbers reported are average state-level estimates based on the full

sample period 2010-2012.

38

Supplemental Table S4: Estimates from Random Intercept Logistic Model (Odds Ratio)

Outcome

All cause full

term infant

mortality

Full term infant

mortality due to

congenital mal-

formations

Full term infant

mortality due

to perinatal

conditions

Full term

infant mor-

tality due to

SUDI

Full term infant

mortality due

to other causes

< High School Reference Reference Reference Reference Reference

High School/College Credit 0.816*** 0.746*** 0.947 0.862*** 0.728***

(0.785,0.847) (0.696,0.799) (0.837,1.071) (0.816,0.911) (0.659,0.805)

Associate/Bachelor's Degree 0.540*** 0.648*** 0.878 0.351*** 0.581***

(0.513,0.568) (0.595,0.705) (0.758,1.016) (0.320,0.384) (0.511,0.659)

Master's Degree/Doctorate 0.415*** 0.462*** 0.696*** 0.228*** 0.559***

(0.384,0.449) (0.408,0.524) (0.568,0.853) (0.194,0.269) (0.469,0.665)

Diabetes = Yes 0.931* 0.851** 0.869 1.102 0.836*

(0.871,0.995) (0.763,0.949) (0.721,1.047) (0.984,1.236) (0.709,0.986)

Chronic Hypertension = Yes 0.808*** 1.033 0.766 0.648*** 0.857

(0.722,0.905) (0.848,1.259) (0.553,1.061) (0.545,0.771) (0.627,1.170)

Eclampsia = Yes 1.384 2.169* 2.782 0.828 1.112

(0.980,1.955) (1.161,4.051) (0.693,11.179) (0.506,1.356) (0.461,2.681)

Smoking in 1st Trimester = Yes 1.228*** 0.831 0.923 1.679*** 1.095

(1.100,1.371) (0.661,1.045) (0.633,1.347) (1.447,1.949) (0.807,1.485)

Smoking in 2nd Trimester = Yes 1.369*** 0.911 1.341 1.539*** 1.790*

(1.159,1.618) (0.631,1.316) (0.755,2.382) (1.233,1.921) (1.147,2.793)

Smoking in 3rd Trimester = Yes 1.049 0.822 0.937 1.282** 0.701

(0.911,1.207) (0.598,1.130) (0.577,1.523) (1.066,1.543) (0.486,1.011)

Mother's Age < 20 1.144*** 0.807*** 1.297*** 1.343*** 1.129

(1.090,1.201) (0.731,0.891) (1.114,1.510) (1.255,1.437) (0.992,1.286)

Age 20-34 Reference Reference Reference Reference Reference

Age 35-39 0.933* 1.280*** 1.04 0.582*** 0.955

(0.883,0.986) (1.177,1.391) (0.898,1.204) (0.521,0.651) (0.834,1.094)

Age 40-44 1.218*** 2.149*** 1.197 0.520*** 0.748

(1.109,1.337) (1.903,2.428) (0.924,1.552) (0.411,0.657) (0.558,1.004)

Age > 44 1.763*** 3.351*** 0.688 0.351 1.658

(1.323,2.348) (2.386,4.707) (0.220,2.144) (0.113,1.088) (0.787,3.491)

Gestation Age 37 weeks 1.191*** 1.071 1.432*** 1.214*** 1.263***

(1.138,1.247) (0.992,1.156) (1.252,1.638) (1.128,1.306) (1.120,1.423)

38 weeks 1.118*** 1.114** 1.153* 1.108*** 1.116*

(1.075,1.163) (1.039,1.193) (1.024,1.299) (1.043,1.177) (1.008,1.236)

39-40 weeks Reference Reference Reference Reference Reference

39

Outcome

All cause full

term infant

mortality

Full term infant

mortality due to

congenital mal-

formations

Full term infant

mortality due

to perinatal

conditions

Full term

infant mor-

tality due to

SUDI

Full term infant

mortality due

to other causes

41 weeks 1.110*** 1.188** 1.214* 1.043 1.078

(1.049,1.174) (1.071,1.319) (1.035,1.424) (0.957,1.136) (0.933,1.245)

42 weeks 1.123** 1.115 1.307* 1.129 0.93

(1.029,1.227) (0.946,1.314) (1.025,1.668) (0.992,1.285) (0.726,1.192)

Gender = Male 1.325*** 1.222*** 1.125* 1.419*** 1.423***

(1.285,1.367) (1.157,1.290) (1.027,1.233) (1.353,1.489) (1.313,1.543)

Birth Weight < 1500 grams 39.706*** 121.142*** 41.776*** 4.561*** 20.919***

(32.68,48.24) (84.22,174.26) (29.14,59.9) (2.672,7.785) (12.262,35.68)

1500-1999 grams 19.237*** 98.473*** 3.983*** 3.001*** 8.430***

(16.23,22.80) (70.45,137.64) (2.65,5.99) (2.091,4.307) (5.326,13.344)

2000-2499 grams 4.427*** 15.733*** 1.365 2.194*** 3.120***

(3.77,5.19) (11.31,21.89) (0.977,1.906) (1.661,2.898) (2.094,4.650)

2500-2999 grams 1.571*** 3.002*** 0.560*** 1.522** 1.425

(1.344,1.835) (2.163,4.167) (0.411,0.763) (1.167,1.985) (0.973,2.088)

3000-3499 grams 0.915 1.103 0.364*** 1.148 0.911

(0.784,1.067) (0.795,1.531) (0.269,0.492) (0.883,1.494) (0.624,1.329)

3500-3999 grams 0.676*** 0.635** 0.338*** 0.907 0.717

(0.578,0.790) (0.455,0.886) (0.249,0.460) (0.696,1.183) (0.490,1.051)

4000-4499 grams 0.656*** 0.596** 0.394*** 0.854 0.726

(0.554,0.776) (0.414,0.858) (0.280,0.556) (0.644,1.133) (0.482,1.093)

> 4499 grams Reference Reference Reference Reference Reference

Birth Outcome = Single Reference Reference Reference Reference Reference

Twin 0.675*** 0.327*** 1.390** 1.128 0.744*

(0.613,0.742) (0.277,0.386) (1.124,1.719) (0.957,1.329) (0.572,0.967)

Triplet 1.083 0.172* 3.122** 2.553 1.753

(0.619,1.895) (0.043,0.692) (1.429,6.823) (0.631,10.33) (0.431,7.135)

Quadruplet 0.91

dropped

10.223*

(0.121,6.846) (1.324,78.955)

Observations (N) 7,749,466 7,749,430 7,749,430 7,749,430 7,749,466

Notes: Table shows results from multivariable multi-level logistic models where individuals are nested within states, and

state level variation is modeled through state specific random intercepts. Displayed coefficients are odds rations with 95%

confidence intervals in parentheses. * p < 0.10, ** p < 0.05, *** p< 0.01.

40

Supplemental Table S5: Relative risk of cause-specific full-term infant mortality for children

born in states with poor vs. children born in states with good FTIMR

Outcome

Full term infant mortality

due to congenital malfor-

mations

Full term infant mor-

tality due to perinatal

conditions

Full term infant

mortality due

to SUDI

Full term infant

mortality due to

other causes

Born in a state with

FTIMR < 1.75 Reference

Born in a state with

FTIMR > 3.75 1.378*** 1.175 1.808*** 1.404***

(1.152,1.648) (0.984,1.403) (1.589,2.057) (1.169,1.685)

< High School Reference

High School/College

Credit 0.730*** 0.905 0.887 0.692***

(0.651,0.818) (0.743,1.102) (0.770,1.020) (0.597,0.802)

Associate/Bachelor's

Degree 0.579*** 0.825 0.380*** 0.524***

(0.482,0.695) (0.621,1.097) (0.320,0.450) (0.431,0.636)

Master's De-

gree/Doctorate 0.429*** 0.659* 0.259*** 0.496***

(0.357,0.516) (0.475,0.915) (0.227,0.296) (0.426,0.576)

Diabetes = Yes 0.762*** 0.889 1.137 0.934

(0.655,0.888) (0.694,1.138) (0.976,1.324) (0.707,1.234)

Chronic Hyperten-

sion = Yes 0.842 0.701 0.791* 0.656*

(0.590,1.202) (0.397,1.238) (0.634,0.987) (0.446,0.964)

Eclampsia = Yes 1.475 Dropped

0.573* 2.747

(0.818,2.662) (0.372,0.883) (0.346,21.807)

Smoking in 1st Tri-

mester = Yes 0.715 0.732 1.814* 0.974

(0.453,1.127) (0.280,1.908) (1.106,2.973) (0.463,2.050)

Smoking in 2nd Tri-

mester = Yes 0.991 1.545 1.479* 1.537

(0.558,1.762) (0.307,7.790) (1.043,2.097) (0.402,5.880)

Smoking in 3rd Tri-

mester = Yes 0.854 0.956 1.242 0.94

(0.562,1.298) (0.250,3.655) (0.714,2.162) (0.434,2.034)

Mother's Age < 20 0.732** 1.542*** 1.449*** 1.054

(0.607,0.882) (1.288,1.846) (1.236,1.698) (0.833,1.332)

Age 20-34 Reference

Age 35-39 1.170*** 0.976 0.588*** 0.897

(1.086,1.261) (0.807,1.181) (0.542,0.639) (0.792,1.015)

41

Outcome

Full term infant mortality

due to congenital malfor-

mations

Full term infant mortali-

ty due to perinatal condi-

tions

Full term in-

fant mortality

due to SUDI

Full term infant

mortality due to

other causes

Age 40-44 1.821*** 1.444 0.434*** 0.645

(1.420,2.336) (0.979,2.128) (0.306,0.616) (0.349,1.192)

Age > 44 2.857*** 0.453 Dropped

1.836

(2.136,3.822) (0.106,1.927) (0.838,4.024)

Gestation Age 37

weeks 1.099 1.550*** 1.204*** 1.295*

(0.966,1.250) (1.256,1.912) (1.086,1.334) (1.027,1.635)

38 weeks 1.157** 1.303** 1.092* 1.036

(1.037,1.291) (1.105,1.537) (1.005,1.186) (0.923,1.162)

39-40 weeks Reference

41 weeks 1.179 1.159 0.985 1.13

(0.969,1.435) (0.956,1.406) (0.899,1.079) (0.950,1.345)

42 weeks 1.144 1.669* 1.087 1.08

(0.879,1.490) (1.010,2.757) (0.920,1.284) (0.736,1.585)

Gender = Male 1.223*** 1.198* 1.426*** 1.366***

(1.133,1.320) (1.043,1.375) (1.341,1.517) (1.185,1.574)

Birth Weight < 1500

grams 124.337*** 43.195*** 4.931*** 33.995***

(73.356,210.749) (18.124,102.949) (2.228,10.916) (14.929,77.407)

1500-1999 grams 96.257*** 2.709*** 2.746** 14.007***

(61.045,151.778) (1.814,4.047) (1.492,5.054) (6.405,30.634)

2000-2499 grams 16.095*** 1.780* 2.570** 5.287***

(10.416,24.869) (1.081,2.931) (1.425,4.636) (2.819,9.913)

2500-2999 grams 3.014** 0.602* 1.685* 2.280**

(1.550,5.863) (0.401,0.901) (1.035,2.745) (1.352,3.846)

3000-3499 grams 1.139 0.424*** 1.35 1.493

(0.587,2.210) (0.263,0.684) (0.822,2.216) (0.874,2.551)

3500-3999 grams 0.777 0.416*** 1.005 1.307

(0.381,1.584) (0.248,0.699) (0.601,1.681) (0.754,2.264)

4000-4499 grams 0.677 0.517** 0.887 1.287

(0.326,1.406) (0.330,0.810) (0.500,1.572) (0.820,2.021)

> 4499 grams Reference

Birth Outcome =

Single Reference

Twin 0.306*** 1.316 1.098 0.769

(0.224,0.418) (0.704,2.462) (0.841,1.434) (0.561,1.054)

Triplet 0.257 Dropped

7.976 2.509

(0.029,2.286) (0.916,69.433) (0.698,9.021)

Quadruplet Dropped

42

Outcome

Full term infant mortality

due to congenital malfor-

mations

Full term infant mortali-

ty due to perinatal condi-

tions

Full term in-

fant mortality

due to SUDI

Full term infant

mortality due to

other causes

Constant 0.000*** 0.001*** 0.001*** 0.000***

(0.000,0.001) (0.000,0.001) (0.000,0.002) (0.000,0.001)

Number of observa-

tions 3,385,884 3,380,110 3,378,831 3,385,884

Notes: Table shows results from multivariable logistic models comparing children born in states with poor FTIMR (FTIMR >

2.75) to states with good FTIMR (< 1.75). Displayed coefficients are odds rations with 95% confidence intervals in parenthe-

ses. Standard errors are clustered at the state level. * p < 0.10, ** p < 0.05, *** p< 0.01.