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Abstract

We use temperature variation within narrowly defined geographic and demographic cells to show that exposure to extreme temperature increases the risk of maternal hospitalization during pregnancy. This effect is driven by emergency hospitalizations for various pregnancy complications, suggesting that it represents a deterioration in underlying maternal health rather than a change in women’s ability to access health care. The effect is larger for black women than for women of other races, suggesting that without significant adaptation, projected increases in extreme temperatures over the next century may further exacerbate racial disparities in maternal health.

I.  Introduction

The United States has experienced a deterioration in maternal pregnancy- and childbirth-related health over the last several decades (Kassebaum et al. 2016), and the burden of health complications is not borne equally by all mothers. For instance, black women are 3.3 times more likely to die from a pregnancy-related cause than their white counterparts (Petersen et al. 2019). Most discussions about maternal health have focused on the role of the health-care system, but we know much less about other—environmental—determinants of maternal health or the disparities within it.1 This paper studies the impact of an environmental factor that is becoming increasingly relevant because of the growing scientific consensus that climate change is contributing to an increase in extreme weather events, especially those linked to heat.2

Specifically, we estimate the causal effect of exposure to extreme temperature during pregnancy on maternal hospitalizations, using the universe of administrative inpatient discharge records from three US states: Arizona, New York, and Washington. We leverage variation in temperature within narrowly defined geographic and demographic cells, and control for birth-county × birth-month × race fixed effects, birth-state × birth-year fixed effects, and a quadratic time trend interacted with birth-county × birth-month indicators.

A growing literature demonstrates that accounting for adaptation is important for measuring the effects of temperature and climate change more broadly (Deschênes and Greenstone 2011; Graff Zivin and Neidell 2014; Barreca et al. 2015, 2016; Carleton et al. 2018). Individuals in historically hotter places may adapt to high temperatures through the adoption of mitigating technologies such as air conditioning and behavioral responses such as spending more time indoors. As such, temperature deviations from the local area norm may be particularly important, and we model exposure to extreme temperature in terms of standard deviations relative to each county’s temperature mean in every month. In addition, as an alternative way of capturing the role of adaptation, we present estimates from models that include different bins of absolute temperature levels, separately for historically cooler and hotter counties.

We find that exposure to extreme temperature has adverse impacts on women’s pregnancy health. We estimate that an additional day during pregnancy with an average temperature that is at least three standard deviations above the county’s monthly mean (hereafter referred to as “above-3-SD” temperature) increases the likelihood that a woman is hospitalized during pregnancy by 0.1 percentage points, which is a 2.2 percent effect at the sample mean. This effect is driven by hospitalizations for emergency and urgent reasons, suggesting that it represents a deterioration in underlying maternal health rather than a change in women’s ability to access health care. Analysis of primary diagnosis codes further indicates that this effect is driven by hospitalizations for various pregnancy complications, such as hypertension, which can be life-threatening.3

Interestingly, we find adverse impacts on maternal hospitalizations from exposure to unusually warm temperatures in both the summer and the non-summer months. Thus, behavioral responses to temperature deviations from the norm—such as engaging in more outdoor physical activity on unusually warm winter days and risking fatigue and/or dehydration—may contribute to explaining the maternal health impacts. Analysis of secondary diagnosis codes in the inpatient data shows that above-3-SD temperatures increase hospitalizations with secondary diagnoses for diseases associated with the urinary and digestive systems (including kidney and urinary tract infections, and gallbladder and liver conditions), for which dehydration is a known risk factor.

Results from models with absolute temperature bins further indicate that accounting for adaptation is important. We split our sample into mothers residing in counties with above- and below-median average temperatures during the analysis period, and find that, in the historically cooler counties, an additional day during pregnancy with a mean temperature of at least 90°F increases the likelihood of an emergency or urgent maternal hospitalization by 0.1 percentage points (5.1 percent at the subsample mean). By contrast, the effect of such a hot day in the historically hotter counties is very small and statistically insignificant. This finding suggests that, because historically cooler places are likely less adapted to extreme heat than historically hotter areas, mothers residing in cooler places bear a disproportionate cost to their pregnancy health. These findings, together with results on exposure to above-3-SD temperature, imply that extreme heat is detrimental to women’s pregnancy-related health when it constitutes a deviation from the local area norm.

We also demonstrate that the health cost of extreme temperature is not distributed equally across racial groups. For non-Hispanic black women, an additional day with above-3-SD temperature during pregnancy raises the likelihood of hospitalization by 0.4 percentage points, or 5 percent. For non-Hispanic white women, the corresponding magnitude is a 0.1 percentage point increase, or 2.4 percent. We find even smaller and statistically insignificant impacts of above-3-SD temperature for Hispanic women and women of other races. Moreover, we find that unusually cold days—those with an average temperature that is at least three standard deviations below the county’s monthly mean—are also detrimental for black women’s pregnancy health. An additional day with below-3-SD temperature during pregnancy raises black women’s likelihood of hospitalization by 0.4 percentage points (5 percent).

Our study contributes to a burgeoning literature, which has identified adverse short-term impacts of extreme temperature on several outcomes, including elderly mortality (Deschênes and Moretti 2009; Deschênes and Greenstone 2011), population-level emergency department visits and hospitalizations (Green et al. 2010; White 2017), and cognitive performance (Cho 2017; Garg, Jagnani, and Taraz 2018; Goodman et al. 2018; Graff Zivin, Hsiang, and Neidell 2018). Multiple studies have further documented negative effects of in utero heat exposure on birth outcomes—including birth weight, gestation length, and the probability of stillbirth (e.g., Deschênes, Greenstone, and Guryan 2009; Dadvand, Basagana, and Sartini 2011; Schifano et al. 2016; Auger et al. 2017; Ha, Liu, Zhu, Kim, Sherman, Grantz, and Mendola 2017; Ha, Liu, Zhu, Kim, Sherman, and Mendola 2017; Kuehn and McCormick 2017; Barreca and Schaller 2020; Bekkar et al. 2020)—highlighting the sensitivity of the prenatal period to extreme heat.4 To the best of our knowledge, only one prior study has analyzed the relationship between prenatal temperature exposure and maternal health, using information on mothers’ pregnancy risk factors and labor/delivery complications reported on birth certificates (Cil and Cameron 2017). However, as multiple validation studies indicate that maternal pregnancy risk factors, obstetric procedures, and complications of labor and delivery are heavily underreported on birth certificates (Parrish et al. 1993; Buescher et al. 1993; Piper et al. 1993; Dobie et al. 1998; Reichman and Hade 2001; DiGiuseppe et al. 2002; Roohan et al. 2003; Lydon-Rochelle et al. 2005), and the degree of underreporting varies with maternal demographic characteristics and birth outcomes (Reichman and Schwartz-Soicher 2007), analyses of maternal health based on birth records data are likely subject to bias from nonrandom measurement error. We address this issue by instead using inpatient discharge records that provide more accurate information on maternal health based on diagnoses associated with each hospitalization. Further, our empirical strategy explores the role of adaptation, which we find to be important for understanding the effects of extreme temperature on women’s pregnancy health.

Our findings suggest that the projected increase in extreme weather over the next century may contribute to further worsening of maternal health. At the same time, adaptation can play an important role in mitigating these impacts—in our analysis, hot temperatures appear to only be damaging to maternal health if they are unusual. Moreover, since black women are more likely to both experience extreme temperature during pregnancy (because of historical differences in housing policies and residence locations; see Hoffman, Shandas, and Pendleton 2020) and have less access to adaptive technologies such as air conditioning (O’Neill, Zanobetti, and Schwartz 2005; Gronlund 2014), our estimates imply that climate change could further exacerbate racial inequities in maternal health.

II.  Data

A.  HCUP State Inpatient Databases

Our data come from the State Inpatient Databases (SID) from the Healthcare Cost and Utilization Project (HCUP). The SID are state-specific files that contain the universe of inpatient records from participating states. Since the availability of variables varies across states and years, we focus on three states that contain all three of the key variables necessary for our analysis: (1) patient county of residence, (2) admission month, and (3) encrypted person identifiers to track patients over time in the same state. Our resulting sample consists of 2.72 million inpatient records of 2.24 million mothers from Arizona (2003 to 2007), New York (2003 to 2013), and Washington (2003 to 2013).

We use diagnosis codes to identify inpatient visits associated with childbirth.5 Since less than 2 percent of all births occur outside of hospitals during our analysis time period, we observe the near-universe of all mothers giving birth in our analysis states.6 We identify maternal hospitalizations during pregnancy by tracking women’s inpatient visit records that occurred in the nine months before delivery. The primary and secondary diagnosis codes associated with each visit allow us to investigate the causes for hospitalization.

B.  NOAA Weather Data

To measure temperature exposure, we obtain data from the National Oceanic and Atmospheric Administration (NOAA). We have information on the mean, maximum, and minimum daily ground temperature and precipitation levels for every county and year-month during our analysis time frame.7 We then merge these data to the maternal inpatient records, using information on the mother’s county of residence at the time of delivery. We use the mother’s year and month of delivery to assign exposure to temperature during pregnancy by assuming a 40-week pregnancy duration for all observations.8

To account for the large amount of variation in average temperatures across geographic areas that could generate differing adaptation responses, we normalize temperature relative to the overall average in each county-by-calendar-month. Specifically, we first calculate the average temperature for every county-month (e.g., July in Queens County, NY), using data from all available years. Then, for every month in all county-year combinations (e.g., July 2012 in Queens County, NY), we calculate the difference between the given month’s mean temperature and the overall average for that county-month, and divide by the standard deviation. We thus obtain a z-score that allows us to classify each month in any given county-year based on its deviation from the overall county-month average. This normalization enables us to identify extreme weather while accounting for long-term adaptation to historical temperature trends.

In addition, we measure exposure based on the number of days during pregnancy that fall into each of 10 absolute temperature bins, ranging from less than 10°F to 90°F or more. To examine the role of adaptation in these models, we study differences between mothers residing in counties with below- and above-median daily mean temperatures averaged over the whole data period.

C.  Distribution of Temperature Exposure and Sample Means

Figure 1 shows the distribution of daily average temperature in Arizona, New York, and Washington during our sample period. We compute the average number of days during pregnancy falling into each of the temperature bins expressed using standard deviations (Figure 1A) and absolute temperature thresholds (Figure 1B). As reported in panel A of Table 1, the average pregnant woman experiences 5.6 days with mean temperature between two and three standard deviations above the county-month mean, and 0.2 days with mean temperature of at least three standard deviations above the county-month mean. Using absolute temperatures, the average pregnant woman experiences 3.9 days with mean temperature falling between 80°F and 90°F, and 0.8 days with mean temperature above 90°F.

Figure 1. 
Figure 1. 

Distribution of daily average temperature during pregnancy. This figure shows the overall average number of days during pregnancy falling into each of the SD and absolute temperature bins denoted on the x-axis. We compute daily average temperature by taking the average of minimum and maximum temperature in a given day measured at weather stations in Arizona (2003–07), New York (2003–13), and Washington (2003–13). To create SD bins, we normalize the daily average temperature by subtracting the monthly mean and dividing it by standard deviation. Source: NOAA weather data.

Table 1. 

Summary statistics

A. Exposure to temperature extremes
 Average number of days during pregnancy with mean temperature
  Between 2 and 3 SDs5.570
  Above 3 SDs0.240
  Between 80°F and 90°F3.874
  Above 90°F0.830
B. Maternal health outcomes (per 100 mothers)
 Any hospitalization during pregnancy4.530
 Emergency/urgent hospitalization during pregnancy3.078
 Primary diagnoses at prenatal hospitalization
  Pregnancy-related complications (ICD 640–649)4.086
  Hemorrhage in early pregnancy (ICD 640)0.043
  Antepartum hemorrhage (ICD 641)0.226
  Hypertension complications (ICD 642)0.231
  Excessive vomiting in pregnancy (ICD 643)0.243
  Early or threatened labor (ICD 644)1.203
  Late pregnancy (ICD 645)0.008
  Other complications (ICD 646)0.668
  Infectious and parasitic conditions (ICD 647)0.094
  Other current conditions (ICD 648)1.258
  Other conditions (ICD 649)0.111
C. Maternal race (per 100 mothers)
 Non-Hispanic white52.041
 Non-Hispanic black12.469
 Hispanic20.131
 Other15.359
Observations41,715

Sources. NOAA weather data and HCUP databases.

Note. We use daily-level temperature data from NOAA to compute the average temperature exposure during pregnancy in panel A. For panels B and C, we use the data collapsed at the race × birth-county × birth-year-month level. Cell size weights are used.

View Table Image

Panel B of Table 1 provides means of maternal health outcomes that we analyze (expressed as rates per 100 mothers). Approximately 4.5 percent of women are hospitalized during pregnancy, with the most common primary diagnosis being a pregnancy-related complication (ICD-9 codes 640–649). Approximately 3.1 percent of women are hospitalized for emergency or urgent reasons. In panel C of Table 1, we report the race/ethnicity distribution of our sample—the mothers in our data are 52.0 percent non-Hispanic white, 12.5 percent non-Hispanic black, 20.1 percent Hispanic, and 15.4 percent in other racial/ethnic groups.

III.  Empirical Strategy

A robust medical literature discusses the biological mechanisms through which extreme temperature could be damaging to human health, and highlights that it can be particularly risky for pregnant women. The underlying issue is that pregnant women are not able to regulate temperature as efficiently as non-pregnant individuals because of the physiologic changes they undergo during gestation (Schifano et al. 2016), which means that both elevated and lowered body temperature during pregnancy can lead to various complications. Heat exposure can alter placental blood flow patterns, which can reduce the integrity of the placenta and increase the chance of abruption (He et al. 2018). Both extreme heat and extreme cold can raise the likelihood of other serious pregnancy complications, including hypertension, preeclampsia, and prolonged premature rupture of membranes (Beltran, Wu, and Laurent 2014; Yackerson, Piura, and Friger 2007). In addition, elevated temperature can increase the fetal heart rate and lead to uterine contractions (Vaha-Eskeli and Erkkola 1991). All of these issues can translate into women needing to be hospitalized during pregnancy.

The goal of this paper is to quantify the causal relationship between extreme temperature and maternal health. A central challenge is that exposure to extreme weather is not randomly assigned. For instance, several studies have documented differences in the health and human capital outcomes of children born in different months of the year because of selection into conception based on parental characteristics and differential exposure to seasonal factors such as the influenza virus (Buckles and Hungerman 2013; Currie and Schwandt 2013). In addition, there is nonrandom sorting of families into hotter and colder regions of the country based on incomes, preferences, and other characteristics, suggesting that cross-sectional comparisons between mothers residing in different regions are unlikely to isolate the causal effects of temperature exposure from the influences of other factors.

A.  Econometric Approach

To address this challenge, we follow the prior literature by leveraging temperature variation within narrowly defined geographic and demographic cells, and flexibly accounting for local outcome trends. We first collapse our data into cells defined by all possible combinations between the mother’s county of residence at delivery, the year-month of childbirth, and race/ethnicity categories (non-Hispanic white, non-Hispanic black, Hispanic, Asian American, Native American, and other). We then use the following regression model to estimate the effects of exposure to extreme temperature during pregnancy:

(1),Yc,y,m,r=α+j=1,j5or78or10βjTempc,y,mj+γf(Precipc,y,m)+θc,m,r+ηy,s(c)+δc,m×f(y)+ϵc,y,m,r
where Yc,y,m,r is an outcome for a mother residing in county c, giving birth in year y and month m, in race/ethnicity group r. We rescale the outcomes by multiplying by 100 (e.g., the number of mothers admitted to the hospital during pregnancy per 100 mothers). The variables Tempc,y,mj represent the number of days during pregnancy falling into each (j) of the eight bins of standard deviations of temperature from the county-month average, ranging from less than −3 SDs to 3 SDs or more, as illustrated in Figure 1A. In alternative specifications, we instead use the number of days during pregnancy falling into each of the 10 bins of absolute temperature ranges, from less than 10°F to 90°F or higher, as shown in Figure 1B.

In the relative temperature exposure models, we omit the [0,1) SD bin as the reference group, while in the absolute temperature exposure models, we omit the [60, 70)°F bin. Thus, the βj coefficients can be interpreted as estimates of the impact of an additional day in a given temperature range j relative to a day in either the [0,1) SD range or the [60, 70)°F range. We are particularly interested in coefficients on the effects of an additional above-3-SD day during pregnancy (β8 in the relative exposure model) and an additional above-90-degree day during pregnancy (β10 in the absolute exposure model).

We control for indicators for the bottom and the top terciles of mean precipitation during pregnancy, f(Precipc,y,m). θc,m,r are fixed effects for every birth-county × birth-month × race cell. ηy,s,(c) are birth-state × birth-year fixed effects, which account for differential outcome trends across states, any state time-varying policies, and the fact that we observe states in different sets of years in the HCUP data. δc,m×f(y) are county-by-calendar-month-specific trends (e.g., Queens-County-by-July-specific trends), which we model with a quadratic polynomial. To further account for potential sorting based on temperature, we control for the share of mothers in each cell whose zip codes of residence fall into different quartiles of the state’s median income distribution.9 We weight all regressions by cell size.10 Because weather is highly spatially correlated, we cluster our standard errors on the commuting zone level.11

Our model identifies the effects of extreme temperature exposure using year-to-year deviations in temperature from the county-month trend within each cell. As a concrete example, consider a black woman giving birth in Queens County, New York, in July 2010 and a black woman giving birth in the same county in July 2011. Our empirical strategy leverages the arguably exogenous difference between them in the temperature deviation during their pregnancies from the Queens-specific quadratic trend among all July births, while controlling for the average difference in temperature exposure between all New York State births in 2010 and 2011.

A potential concern for our empirical design is that there remains insufficient variation in temperature exposure after we condition on all of the fixed effects and trends just described. In Appendix Figures A.1(a) and (c), we plot histograms of the residuals from a regression of the number of days of pregnancy exposure to above-3-SD temperature and above-90-degree temperature, respectively, on the birth-county × birth-month × race fixed effects, birth-state × birth-year fixed effects, and county × calendar-month-specific quadratic trends. The graphs show that there is more residual variation in the top relative temperature bin than in the top absolute temperature bin. In addition, as expected, there is more residual variation in extreme temperatures than in more typical temperatures (Appendix Figures A.1(b) and (d)). That said, we present estimates from both the relative and the absolute exposure models, and we show that our results are not sensitive to excluding different fixed effects and trends.

B.  Identifying Assumption

Our estimates of βj represent causal effects of pregnancy exposure to temperature under the assumption that the within-cell variation in temperature (conditional on birth-state × birth-year fixed effects and county × calendar-month trends) is uncorrelated with other determinants of maternal health. While this assumption is inherently untestable, we present some indirect tests to assess its plausibility.

First, we check whether there is any systematic relationship between the temperature variation and population demographic characteristics. We collapse our data to the birth-county × birth-year × birth-month level, and estimate a version of equation 1, excluding controls for demographic characteristics and maternal zip code income quartiles. Panel A of Appendix Table B.1 shows that temperature exposure is not correlated with the racial/ethnic composition of mothers in our data. In panel B of Appendix Table B.1, we do not observe any significant relationship between exposure to different temperature bins during pregnancy and the share of mothers residing in zip codes in each quartile of the median income distribution.12

Second, to address the possibility that extreme temperature could influence fertility rates and thus affect selection into our sample of analysis, we estimate models that use the number of births in each county-month cell as the outcome.13Appendix Table B.2 shows that none of the temperature bins is correlated with the number of births in our analysis.

Third, we test the robustness of our results to including hypothetical exposure to temperature assuming a mother gave birth either one or two years before her actual delivery year-month, or one year after her actual delivery year-month. As we show below, the main effects of exposure during pregnancy remain strong and significant even when we add lags and leads of temperature exposure.

IV.  Results

A.  Results from Models with Relative Temperature Bins

Table 2 and Figure 2 show that exposure to extreme temperature raises the likelihood that a mother is hospitalized during pregnancy. Specifically, we find that an additional day with above-3-SD temperature during pregnancy raises the likelihood that a mother is hospitalized by 0.1 percentage points, which translates into a 2.2 percent effect size when evaluated at the sample mean. In column 2 of Table 2, we show that the increase in prenatal hospitalizations is driven by visits for emergency and urgent reasons rather than by scheduled admissions, which suggests that the effect represents a deterioration in underlying maternal health as opposed to an improvement in health-care access or utilization. Moreover, while the effect of exposure to above-3-SD temperature is particularly strong, we also find some evidence that exposure to days with temperature between 2 and 3 SDs above the county-month mean increases maternal hospitalizations during pregnancy (by 0.01 percentage points, or 0.3 percent). When we explore the effects of trimester-specific exposure in Appendix Figure A.2, we find that above-3-SD temperature during the second and third trimesters raises the likelihood of hospitalization by 0.22 and 0.12 percentage points, respectively.14

Table 2. 

Effects of temperature deviations during pregnancy on maternal hospitalization

 Prenatal hospitalization
 AnyEmergency/urgent
 (1)(2)
# days below −3 SD temp0.0230.023
 (0.042)(0.027)
# days between −3 and −2 SD temp−0.001−0.001
 (0.008)(0.006)
# days between −2 and −1 SD temp0.003−0.001
 (0.002)(0.003)
# days between −1 and 0 SD temp−0.002−0.004b
 (0.002)(0.002)
# days between 1 and 2 SD temp−0.006b−0.009a
 (0.002)(0.002)
# days between 2 and 3 SD temp0.014b0.011
 (0.005)(0.007)
# days above 3 SD temp0.101a0.071a
 (0.033)(0.024)
Observations41,71541,715
Adjusted R20.4740.501
Mean4.5303.078

Source. HCUP SID merged with NOAA weather data.

Note. This table reports regression coefficients (βj) from equation 1. Robust standard errors, clustered by commuting zone, are in parentheses. Each outcome is rescaled by multiplying by 100. All regressions control for mother’s race/ethnicity × birth-county × birth-month fixed effects, zip code–level income quartiles, birth-state × birth-year fixed effects, a quadratic time at the county × calendar-month level, and a series of indicators for terciles of precipitation in each trimester. We use the data collapsed at the race × birth-county× birth-year-month level. Cell size weights are used.

a p < 0.01.

b p < 0.05.

c p < 0.10.

View Table Image
Figure 2. 
Figure 2. 

Effect of temperature deviations during pregnancy on maternal hospitalization. The figure plots regression coefficients (βj) from equation 1 for each SD bin (j) with 95 percent confidence intervals. The outcome is rescaled by multiplying by 100. Standard errors are clustered by the commuting zone level. The estimation controls for mother’s race/ethnicity × birth-county × birth-month fixed effects, zip code–level income quartiles, birth-state × birth-year fixed effects, a quadratic time at the county × calendar-month level, and a series of indicators for terciles of precipitation in each trimester. We use the data collapsed at the race × birth-county × birth-year-month level. Cell size weights are used.

Next, we examine the primary causes for maternal hospitalizations. In Figure 3, we present coefficient estimates and 95 percent confidence intervals on exposure to above-3-SD temperature from models that use indicators for various primary diagnoses codes associated with the pregnancy hospitalization as outcomes. We find that the increase in maternal hospitalizations in response to extreme temperature is driven by a range of pregnancy complications, including hypertension (ICD 642) and excessive vomiting (ICD 643), as well as a range of other (less common) complications and conditions (ICD 646, 648, 649). Appendix Table B.3 shows the full set of coefficients corresponding to each SD bin in our regression model.

Figure 3. 
Figure 3. 

Effects of above-3-SD temperature during pregnancy on maternal hospitalization, by primary diagnosis categories. The figure plots separate regression coefficients (β8) from equation 1 for exposure to above-3-SD temperature during pregnancy with 95 percent confidence intervals for each diagnosis category. Outcomes are rescaled by multiplying by 100. ICD codes 640–649 indicate “complications mainly related to pregnancy.” The definition of each subcategory is as follows. ICD 640: Hemorrhage in early pregnancy. ICD 641: Antepartum hemorrhage abruptio placentae and placenta previa. ICD 642: Hypertension complicating pregnancy childbirth and the puerperium. ICD 643: Excessive vomiting in pregnancy. ICD 644: Early or threatened labor. ICD 645: Late pregnancy. ICD 646: Other complications of pregnancy not elsewhere classified. ICD 647: Infectious and parasitic conditions in the mother classifiable elsewhere but complicating pregnancy, childbirth, or the puerperium. ICD 648: Other current conditions in the mother classifiable elsewhere but complicating pregnancy, childbirth, or the puerperium. ICD 649: Other conditions or status of the mother complicating pregnancy, childbirth, or the puerperium. Standard errors are clustered by the commuting zone level. All regressions control for mother’s race/ethnicity × birth-county × birth-month fixed effects, zip code–level income quartiles, birth-state × birth-year fixed effects, a quadratic time at the county × calendar-month level, and a series of indicators for terciles of precipitation in each trimester. We use the data collapsed at the race × birth-county × birth-year-month level. Cell size weights are used.

We also explore the impacts of season-specific temperature deviations by estimating regression models that include season-specific relative temperature exposure bins. Specifically, we include bins for summer months (May–September), bins for spring and fall months (March–April and October–November), and bins for winter months (December–February). Appendix Figure A.3 presents the results. The confidence intervals for the estimates on the effects of exposure to above-3-SD temperatures in the three seasonal categories overlap, so we cannot attribute our impacts to being driven by any one season. That said, it does appear that abnormally warm temperatures in non-summer months increase the likelihood of maternal hospitalization during pregnancy. This suggests that behavioral mechanisms may contribute to explaining our maternal health impacts. For instance, pregnant women may spend more time outdoors in response to unusually warm temperatures in non-summer months, potentially increasing their risk of contracting an infection due to exposure to more people than they would have otherwise encountered. Alternatively, pregnant women may engage in more outdoor physical activity in warmer weather during non-summer months, leading to a greater risk of fatigue and/or dehydration.

While we do not have any data to precisely explore these behavioral channels, we can analyze secondary diagnosis codes in the hospitalizations data that are reported alongside the primary codes shown in Figure 3. For this analysis, we focus on the most common secondary diagnoses other than pregnancy complications. Appendix Table B.4 presents results from regression models that use as outcomes indicators for prenatal hospitalizations associated with various categories of secondary diagnoses. We find that exposure to above-3-SD temperatures significantly increases the likelihood of hospitalizations for diseases associated with the urinary system (including kidney and urinary tract infections) and diseases of the digestive system (including gallbladder and liver conditions). Dehydration is a known risk factor for many of these conditions, making it a plausible mechanism for explaining the link between temperature and these hospitalizations.

B.  Differences By Maternal Race and Ethnicity

We find that the effects of extreme temperature on prenatal hospitalization are different across mothers from different racial/ethnic groups.15 Table 3 shows that the estimated adverse effect of extreme temperature is particularly large for non-Hispanic black mothers—we observe that an additional day with either above-3-SD or below-3-SD temperature during pregnancy increases the likelihood of hospitalization by 0.4 percentage points, or 5 percent at the subsample mean. By contrast, for non-Hispanic white mothers, we only find a 0.1 percentage point (2.4 percent) increase in the likelihood of prenatal hospitalization associated with exposure to above-3-SD temperature. The p-values from tests of the difference in coefficients on exposure to above-3-SD and below-3-SD temperature between white and black mothers are 0.018 and <0.001, respectively.

Table 3. 

Effects of temperature deviations during pregnancy on maternal hospitalization, by race/ethnicity

 Any prenatal hospitalization
 WhiteBlackHispanicOther
 (1)(2)(3)(4)
# days below −3 SD temp−0.0240.360a0.189c−0.045
 (0.073)(0.101)(0.106)(0.101)
# days between −3 and −2 SD temp0.009−0.034−0.047a0.005
 (0.009)(0.030)(0.013)(0.018)
# days between −2 and −1 SD temp0.002−0.0010.012−0.003
 (0.004)(0.005)(0.008)(0.008)
# days between −1 and 0 SD temp−0.0010.000−0.006−0.005
 (0.002)(0.005)(0.005)(0.004)
# days between 1 and 2 SD temp−0.004−0.007−0.007−0.019c
 (0.005)(0.004)(0.007)(0.010)
# days between 2 and 3 SD temp0.019b−0.0120.0220.027c
 (0.008)(0.012)(0.027)(0.015)
# days above 3 SD temp0.092b0.394a0.0360.012
 (0.033)(0.136)(0.058)(0.106)
Observations8,5394,4125,02212,497
Adjusted R20.4480.4580.4580.246
Mean3.8667.8855.6963.889

Source. HCUP SID merged with NOAA weather data.

Note. This table reports regression coefficients (βj) from equation 1. Robust standard errors, clustered by commuting zone, are in parentheses. Each outcome is rescaled by multiplying by 100. All regressions control for birth-county × birth-month fixed effects, zip code–level income quartiles, birth-state × birth-year fixed effects, a quadratic time at the county × calendar-month level, and a series of indicators for terciles of precipitation in each trimester. We use the data collapsed at the race × birth-county × birth-year-month level. We drop counties with fewer than 50 mothers in each race/ethnicity category. Cell size weights are used.

a p < 0.01.

b p < 0.05.

c p < 0.10.

View Table Image

C.  Results from Models with Absolute Temperature Bins

While we use relative temperature exposure based on standard deviations relative to each county’s mean to account for adaptation, we also present estimates from more standard models that include absolute temperature bins. Here, we account for differences in local area adaptation by splitting the sample into counties with below- and above-median daily mean temperatures averaged over the whole data period.

Table 4 shows that exposure to temperature 90°F or more is associated with an increase in the likelihood of maternal hospitalization during pregnancy in the historically cooler counties. Specifically, an additional day with above-90-degree temperature increases the likelihood of an emergency or urgent hospitalization during pregnancy by 0.1 percentage points (or 5.1 percent) for mothers in below-median counties. For mothers in the above-median counties, the corresponding coefficient is much smaller and statistically insignificant.16 The difference in the effects on emergency/urgent hospitalizations between mothers in below-median and above-median counties is statistically significant (p-value: 0.042).17

Table 4. 

Effects of absolute temperatures during pregnancy on maternal hospitalization, historically cooler vs. historically hotter counties

 Prenatal hospitalization
 AnyEmergency/urgent
 (1)(2)
 A. Historically cooler counties (below-median average temperatures)
# days below 10°F0.0010.001
 (0.024)(0.017)
# days between 10°F and 20°F−0.004−0.002
 (0.012)(0.008)
# days between 20°F and 30°F0.008−0.002
 (0.008)(0.004)
# days between 30°F and 40°F0.0070.000
 (0.006)(0.002)
# days between 40°F and 50°F0.0040.001
 (0.004)(0.003)
# days between 50°F and 60°F0.0060.002
 (0.004)(0.004)
# days between 70°F and 80°F−0.005−0.006
 (0.004)(0.005)
# days between 80°F and 90°F0.0180.011
 (0.023)(0.023)
# days above 90°F0.0760.111b
 (0.057)(0.044)
Observations20,61120,611
Adjusted R20.2080.188
Mean3.6812.158
 B. Historically hotter counties (above-median average temperatures)
# days below 10°F−0.045b−0.006
 (0.018)(0.020)
# days between 10°F and 20°F0.023b0.012
 (0.009)(0.010)
# days between 20°F and 30°F0.004−0.003
 (0.002)(0.002)
# days between 30°F and 40°F0.001−0.002
 (0.004)(0.002)
# days between 40°F and 50°F0.002−0.002
 (0.002)(0.002)
# days between 50°F and 60°F−0.001−0.004

a p < 0.01.

b p < 0.05.

c p < 0.10.

View Table Image: 1 | 2

V.  Additional Results

A.  Results by State

In Appendix Tables B.6 and B.7, we analyze the effects of relative and absolute temperature exposure separately by state. We find significant effects of both unusually cold and unusually hot days in New York. Our sample size in New York is substantially larger than in Washington or Arizona, providing us with more power to identify statistically significant effects there. Moreover, we note that mothers in Arizona did not experience any above-3-SD days during our analysis period.

When we examine models that include absolute temperature bins separately by state (Appendix Table B.7), we find that the adverse effects of above-90-degree heat are present in both Washington and New York (with a much larger magnitude in Washington than in New York). Very hot days are much rarer in Washington and New York than they are in Arizona, and we find that the impacts on maternal hospitalizations are concentrated in the former states. Taken together, the state-specific results again highlight the importance of adaptation in understanding the effects of extreme temperature.

B.  Sensitivity of Estimates to Including Different Controls

Appendix Table B.8 evaluates how the coefficient estimates change as we include different combinations of the zip code income controls, fixed effects, and trends. The results in column 6 come from our baseline model. The estimated coefficients on exposure to above-3-SD temperature are very similar across all of these different specifications, suggesting that the results in our preferred specification are not driven by a particular choice of fixed effects and trends. Rather, the inclusion of these controls allows us to deliver a conservative but precise estimate of the effect of extreme temperature on maternal health.

C.  Placebo Temperature Exposure

To assess the possibility of bias due to differential trends in temperature exposure that are not controlled for in our main regression models, we test the robustness of our results to including different leads and lags of temperature exposure. We define “lags” as temperature exposure before pregnancy, and “leads” as temperature exposure after pregnancy.

First, we consider two-year lags. That is, for every birth-county × birth-year-month, we calculate the hypothetical exposure to temperature assuming that the child had been born two years prior to his/her actual month of birth. We use a two-year (instead of a one-year) lag to avoid confounding our estimates with possible effects of temperature on conception or fertility (Lam and Miron 1996; Barreca et al. 2015; Wilde, Apouey, and Jung 2017). Table 5 shows that our main results are robust to the inclusion of this placebo control. The point estimate for the effect of actual exposure to extreme temperature does not change when we control for placebo exposure, and the placebo coefficient stays insignificant.

Table 5. 

Robustness to including two-year lags in temperature exposure

 Outcome: Any prenatal hospitalization
 Main specification in the subsample with two-year lagsTwo-year lags onlyAdding two-year lags to the main specification
 (1)(2)(3)
# days below −3 SD temp0.029 0.026
 (0.041) (0.040)
# days between −3 and −2 SD temp−0.004 0.003
 (0.010) (0.008)
# days between −2 and −1 SD temp0.006 0.006
 (0.004) (0.004)
# days between −1 and 0 SD temp−0.002 −0.002
 (0.002) (0.002)
# days between 1 and 2 SD temp−0.000 −0.002
 (0.004) (0.004)
# days between 2 and 3 SD temp0.013b 0.022a
 (0.005) (0.007)
# days above 3 SD temp0.091b 0.083b
 (0.034) (0.033)
# days below −3 SD temp (two-year lags), placebo−0.076c−0.079b 
 (0.043)(0.037) 
# days between −3 and −2 SD temp (two-year lags), placebo0.014c0.011c 
 (0.008)(0.007) 
# days between −2 and −1 SD temp (two-year lags), placebo0.0040.005 
 (0.004)(0.004) 
# days between −1 and 0 SD temp (two-year lags), placebo−0.005c−0.004 
 (0.003)(0.003) 
# days between 1 and 2 SD temp (two-year lags), placebo−0.010a−0.011a 
 (0.002)(0.003) 
# days between 2 and 3 SD temp (two-year lags), placebo0.0130.013 
 (0.012)(0.012) 
# days above 3 SD temp (two-year lags), placebo0.0030.038 
 (0.039)(0.043) 
Observations33,15433,15433,154
Adjusted R20.4580.4580.458
Mean4.5304.5304.530

Source. HCUP SID merged with NOAA weather data.

Note. Column 1 reports regression coefficients (βj) from equation 1 using the subsample of data for which we can calculate two-year lags of exposure (i.e., hypothetical exposure to temperature assuming that the child had been born two years prior to his/her actual month of birth). Column 2 shows results from models that include only two-year lags of exposure and exclude actual exposure. Column 3 shows results from models that include both actual temperature exposure and two-year lags in temperature exposure. Robust standard errors, clustered by commuting zone, are in parentheses. Each outcome is rescaled by multiplying by 100. All regressions control for mother’s race/ethnicity × birth-county × birth-month fixed effects, zip code–level income quartiles, birth-state × birth-year fixed effects, a quadratic time at the county × calendar-month level, and a series of indicators for terciles of precipitation. We use the data collapsed at the race × birth-county × birth-year-month level. Cell size weights are used.

a p < 0.01.

b p < 0.05.

c p < 0.10.

View Table Image: 1 | 2

We also consider one-year lags of temperature exposure to directly test whether fertility effects might be confounding our results. Columns 1 and 2 of Appendix Table B.9 show that our results are robust to including one-year lags, and the coefficients on one-year lags are small and insignificant. In addition, columns 3 and 4 of Appendix Table B.9 present results from models that include one-year leads of temperature exposure (i.e., hypothetical exposure assuming a child is born one year after his/her actual month of birth). We continue to find statistically significant effects of extreme temperature exposure on maternal hospitalizations even after controlling for temperature leads.

D.  Controlling For Air Pollution

Prior research shows that pollution is highly correlated with weather and affects population health (e.g., Ye et al. 2012). To account for possible confounding by air pollution, we estimate our main models with additional controls for air quality index (AQI) categories as measured by the Environmental Protection Agency. Since AQI is not available for all counties and year-months in our analysis sample, we also reestimate our main specifications using a subsample of the data with nonmissing AQI measures. We find that our estimates are robust to including pollution controls (see Appendix Table B.10).

VI.  Conclusion

Scientists predict that global average temperatures will rise over the next 50 to 100 years, mostly because of a shift to the right in the upper tail of the temperature distribution. For instance, the number of days with mean temperature above 90°F in the average American county is forecasted to increase from about one to approximately 43 per year by 2070–99 (Intergovernmental Panel on Climate Change 2014). Understanding the health consequences of this increase in extreme temperature is critical for informing discussions about the costs of climate change and the possible benefits of mitigating policies. Moreover, the growing literature on the importance of adaptation suggests that extreme deviations from typical weather may be particularly damaging.

In this paper, we contribute to the evidence on the costs of exposure to extreme temperature by documenting maternal health impacts. We use the universe of inpatient discharge records from three states and find that exposure to extreme temperature during pregnancy leads to an increase in women’s emergency and urgent hospitalizations for pregnancy-related complications. The fact that the increase in hospitalizations during pregnancy is larger for black mothers than for mothers in other racial groups suggests that climate change may exacerbate the already large racial gap in maternal health.

What do our estimates imply about the economic cost of extreme temperature? We conduct a back-of-the-envelope calculation by scaling our estimate of the impact of an additional day of exposure to above-3-SD temperature by the average number of such days experienced by pregnant women in our sample and by the total number of births in each year in the United States (based on the most recent available vital statistics data from 2018). This calculation suggests that there are approximately 910 additional maternal hospitalizations in the United States resulting from above-3-SD temperatures in every year.18 HCUP data on hospital charges suggest that these excess hospitalizations cost approximately $10,050,040 (in 2018 dollars).19 Similarly, we estimate that an additional day of exposure to temperature between two and three standard deviations relative to the county-month mean results in approximately 2,112 excess hospitalizations, costing around $23,324,747.20 Of course, this calculation does not incorporate other costs associated with the deterioration in women’s pregnancy health (including nonhealth costs such as foregone wages due to missed time at work) and does not capture the impacts of extreme temperature on other outcomes measured in the existing literature.

An important limitation of our study is that we are not able to measure maternal health impacts not captured by the hospitalizations data. Just like measures of maternal health in birth records may miss effects on other aspects of health that we do measure, our estimates based on hospitalizations cannot capture potential impacts on more minor health insults that do not lead to hospital encounters. Future research may expand our understanding of these effects with better data on other health conditions.

We thank Alan Barreca, Janet Currie, Bhash Mazumder, and Ciaran Phibbs, as well as participants at the 2019 American Society of Health Economists annual meeting and the 2020 Allied Social Science Associations (ASSA) annual meeting. We use the State Inpatient Databases from the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality, provided by the Arizona Department of Health Services, the New York State Department of Health, and the Washington State Department of Health. We thank Jean Roth at the National Bureau of Economic Research for assistance with the data.

Notes

Jiyoon Kim, Department of Economics, Swarthmore College. Ajin Lee (corresponding author, ), Department of Economics, Michigan State University. Maya Rossin-Slater, Department of Medicine, Stanford University; NBER; and IZA.

1. For examples of these discussions in the press, see https://www.vox.com/science-and-health/2017/6/26/15872734/what-no-one-tells-new-moms-about-what-happens-after-childbirth; https://www.npr.org/2017/05/12/528098789/u-s-has-the-worst-rate-of-maternal-deaths-in-the-developed-world; and https://www.npr.org/2017/05/12/527806002/focus-on-infants-during-childbirth-leaves-u-s-moms-in-danger.

2. For one resource summarizing the scientific literature surrounding climate change, see: https://www.carbonbrief.org/mapped-how-climate-change-affects-extreme-weather-around-the-world.

3. Hypertensive disorders represent the fifth leading cause of maternal pregnancy-related death (Kuriya et al. 2016).

4. Fetuses and infants are sensitive to extreme heat because of their developing thermoregulatory and sympathetic nervous systems; see Young (2002); Knobel and Holditch-Davis (2007); Xu et al. (2012). Two recent studies have also shown that early life heat exposure has lasting negative effects on long-term cognitive ability (Hu and Li 2019) and adult earnings (Isen, Rossin-Slater, and Walker 2017).

5. We use DRG 370–375 or 765–768 and 774–775, depending on the version of DRG.

6. See https://www.cdc.gov/nchs/products/databriefs/db144.htm for statistics on out-of-hospital births in the United States.

7. We aggregate weather station–level data to the county level by taking an average of all weather stations with nonmissing data in a given county.

8. Information on gestational age is available via diagnosis codes in only a small share of children’s hospitalization records; this information is not available in the maternal inpatient records (and we cannot link mothers to their children in our data). Moreover, using actual pregnancy duration to assign exposure can be problematic because of the possible endogeneity of gestational age with respect to the in utero shock (Currie and Rossin-Slater 2013).

9. The HCUP data include information on the patient’s zip code of residence along with a categorical variable (MEDINCSTQ) that provides the quartile classification of the estimated median household income for the state. According to HCUP, “The cut-offs for the quartile designation for each state is determined using ZIP Code-demographic data obtained from Claritas. The assignment of MEDINCSTQ for a particular discharge is based on the median income of the patient’s ZIP Code. The quartiles are identified by values of 1 to 4, indicating the poorest to wealthiest populations, respectively. Because these estimates are updated annually, the value ranges for the MEDINCSTQ categories vary by year and state.” Source: https://www.hcup-us.ahrq.gov/db/vars/sasddistnote.jsp?var=medincstq.

10. Results based on collapsed data with cell size weights are identical to those using the underlying individual-level data, since we do not have any other individual-level controls.

11. Our results are also robust to using an alternative adjustment of standard errors to reflect spatial dependence, as modeled by Conley (1999) and implemented by Hsiang (2010). Results are available upon request.

12. In supplementary analyses, we have also examined the relationship between temperature and the sex ratio at birth, finding no significant effects (results available upon request). The lack of relationship between temperature exposure and infant sex suggests that there is no detectable effect on miscarriages, as changes in the sex ratio at birth are often used as proxies for changes in miscarriage rates (e.g., Sanders and Stoecker 2015; Halla and Zweimüller 2013).

13. We have used national vital statistics data to compare with the number of births in our data, confirming that our data closely tracks the universe of births.

14. Since we assume a 40-week pregnancy length for all mothers in the analysis, it is worth noting that trimester-specific estimates are subject to measurement error.

15. When we estimate our models separately by race/ethnicity categories, we drop counties that have fewer than 50 mothers in the group under analysis. This sample restriction allows us to identify the effects for each subgroup by providing sufficient variation in temperature exposure conditional on a large set of fixed effects and trends.

16. Thus, when we pool all of the mothers in our data and estimate models using exposure to absolute temperature bins, we find a positive but statistically insignificant average effect on maternal hospitalizations (Appendix Table B.5).

17. Table 4 also shows that, in the historically hotter counties, exposure to days in the 70–80°F and 80–90°F ranges slightly reduces the likelihood of maternal hospitalization during pregnancy. We interpret this as evidence that temperature in “normal” ranges may be advantageous for maternal health, possibly because of adaptation behavior (i.e., these are temperature ranges in which mothers feel most comfortable).

18. This calculation is conducted as follows. We find that each day of exposure to above-3-SD temperature increases maternal hospitalizations by 0.1 percentage points (Table 2, column 1). The average woman in our analysis sample experiences 0.24 such days (Table 1). And according to vital statistics data from 2018, there are 3,791,712 births per year (see https://www.cdc.gov/nchs/fastats/births.htm). Thus, 0.001×0.24×3,791,712=910.

19. According to HCUP, the average charge associated with a hospitalization during pregnancy is $11,044 in 2018 dollars.

20. This calculation is conducted as follows. We find that each day of exposure to the 2–3 SD temperature bin increases maternal hospitalizations by 0.01 percentage points (Table 2, column 1). The average woman in our analysis sample experiences 5.57 such days (Table 1). Thus, 0.0001×5.57×3,791,712=2,112. Multiplying by $11,044, the average charge, yields $23,324,747.

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