Investigating the Aliso Canyon gas blowout disaster and adverse birth outcomes: A quasiexperimental approach

Kimberly C Paul; John Molitor; Yu Yu; Christina Batteate; Massimo Stafoggia; Sanjali Mitra; Qi Meng; Diane Garcia-Gonzales; Myles Cockburn; David Eisenman; Sudipto Banerjee; Honghu Liu; Michael Jerrett; Beate Ritz

doi:10.1126/sciadv.adr6684

. 2025 Sep 12;11(37):eadr6684. doi: 10.1126/sciadv.adr6684

Investigating the Aliso Canyon gas blowout disaster and adverse birth outcomes: A quasiexperimental approach

Kimberly C Paul ^1,^*, John Molitor ², Yu Yu ³, Christina Batteate ⁴, Massimo Stafoggia ⁴, Sanjali Mitra ⁵, Qi Meng ⁴, Diane Garcia-Gonzales ^4,⁶, Myles Cockburn ⁷, David Eisenman ^8,⁹, Sudipto Banerjee ¹⁰, Honghu Liu ^6,^10,¹¹, Michael Jerrett ^4,⁹, Beate Ritz ^1,^4,^5,^*

^¹Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.

^²Oregon State University, Corvallis, OR, USA.

^³UCLA Center for Health Policy Research, UCLA, Los Angeles, CA, USA.

^⁴Department of Environmental Health Sciences, Fielding School of Public Health, UCLA, Los Angeles, CA, USA.

^⁵Department of Epidemiology, Fielding School of Public Health, UCLA, Los Angeles, CA, USA.

^⁶Section of Public and Population Health, School of Dentistry, UCLA, Los Angeles, CA, USA.

^⁷Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

^⁸Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.

^⁹Center for Public Health and Disasters and Center for Healthy Climate Solutions, Fielding School of Public Health, UCLA, Los Angeles, CA, USA.

^¹⁰Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA, USA.

^¹¹Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.

Corresponding author. Email: kimberlp@ucla.edu (K.C.P.); britz@ucla.edu (B.R.)

Roles

Kimberly C Paul: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing

John Molitor: Conceptualization, Data curation, Formal analysis, Methodology, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing

Yu Yu: Data curation, Formal analysis, Resources, Writing - review & editing

Christina Batteate: Project administration, Resources, Visualization, Writing - review & editing

Massimo Stafoggia: Methodology, Validation, Writing - review & editing

Sanjali Mitra: Data curation, Validation, Writing - review & editing

Qi Meng: Data curation, Investigation, Writing - review & editing

Diane Garcia-Gonzales: Funding acquisition, Writing - original draft, Writing - review & editing

Myles Cockburn: Conceptualization, Formal analysis, Methodology, Resources, Writing - review & editing

David Eisenman: Funding acquisition, Visualization, Writing - review & editing

Sudipto Banerjee: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing

Honghu Liu: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing - review & editing

Michael Jerrett: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing

Beate Ritz: Conceptualization, Methodology, Project administration, Resources, Supervision, Validation, Writing - review & editing

PMCID: PMC12428921 PMID: 40938976

Abstract

On 23 October 2015, operators at the Aliso Canyon Natural Gas Storage field in Northern Los Angeles reported an uncontrolled natural gas leak. The blowout persisted for 112 days, releasing ~109,000 metric tons of methane into the atmosphere. Elevated air toxics and fine particle pollutant levels were also measured in nearby communities. We used California’s birth records and a quasiexperimental design to assess whether pregnant women living in affected communities during the disaster experienced more adverse birth outcomes than expected. Overall, the prevalence of low birthweight and term low birthweight were 45 to 100% higher than expected among women living in the affected communities whose late pregnancy overlapped with the blowout. The strongest effects were observed among women living directly south and southwest of the facility. Furthermore, we observed a dose-response effect, where the odds of low birthweight were highest among women living closest to the well and attenuated out.

Late pregnancy exposure to the Aliso Canyon gas blowout is linked to higher rates of low birthweight and term low birthweight.

INTRODUCTION

On 23 October 2015, Southern California (SoCal) Gas operators at the Aliso Canyon Natural Gas Storage field reported an uncontrolled, fugitive natural gas leak at well site SS-25, which is a high-pressured well located less than 1 mile (1.61 km) upwind from the Porter Ranch community. The storage field, located in Los Angeles County’s Santa Susana Mountains, is the second-largest natural gas storage facility in the western United States with a total working storage capacity of 2.4 billion m³ (86 billion ft³) (1). The gas blowout persisted for 112 days until 11 February 2016, when state officials announced the leak was halted and then permanently sealed on 18 February 2016. Before this point, approximately 109,000 metric tons of methane, the main constituent of natural gas, had been released into the atmosphere (1, 2). At its peak, an estimated 58,000 kg (±12,000) of methane was released per hour (3). This was comparable to the emission rates of the entire oil and gas production industry in the US or on par with the daily emissions of 4.5 million cars (1, 4). To date, this is the largest anthropogenic release of natural gas from a point source ever recorded in the United States (1, 2). Hundreds of thousands of people lived downwind of the leaking well during the blowout (≤10 km). Because of health concerns, two schools in the area were closed for the entire remaining school year (Porter Ranch Community School, 1363 students; Castlebay Lane Elementary, 707 students) (5, 6). Nearly 10,000 people living in the closest communities were temporarily evacuated to areas away from the disaster site. Thousands of people reported health symptoms, including many residents who reported ongoing health issues after returning home (7).

Natural gas is primarily composed of methane and ethane (CH₄ and C₂H₆). Airborne chemical monitoring during the blowout showed exceptionally high concentrations of both in the densely populated areas south of the well in the San Fernando Valley (1). Odorants, which are added to natural gas, typically mercaptans including tetrahydrothiophene and t-butyl mercaptan, were also part of the exposure profile (1). During the blowout, elevated levels of hazardous air pollutants (HAPs) and sulfides were also measured in ambient air and a visible oily residue was reported at locations across the adjacent Porter Ranch community (8–11). As we have previously reported, on the basis of concentrations of volatile organic compounds (VOCs) measured at distances between 1.2 and 7.3 km south of the well, which were correlated with concurrent CH₄ measurements, a broad range of HAPs were coemitted along with methane during the blowout (8). South Coast Air Quality Management District (SCAQMD) ground canister samples from December 2015 showed that several HAP compound levels were highly influenced by blowout emissions, including n-hexane, styrene, toluene, and benzene (8). This was in line with the airborne monitoring, which also detected trace enhancements of benzene, toluene, ethylbenzene, and xylene (BTEX) compounds (1), and the Los Angeles County Department of Public Health (LACDPH) led air monitoring, which found transient elevations of benzene, airborne particles, and methane south of the well (12). In addition, some measurements taken on-site a few hundred meters downwind of the blowout showed incredibly high levels of benzene, some of which likely flowed into the residential areas of the community, when dominant northerly winds were present. These on-site data suggest a strong potential for off-site impacts from the leak, given the extraordinary concentrations observed near the leak (13, 14).

Extensive indoor air and dust sampling conducted in 114 homes during April 2016, after the leak was plugged, also detected an unusual cluster of metals such as barium, antimony, strontium, and manganese in many homes (15). These metals were part of the muds injected into the well in attempts to kill the leak, which resulted in several shelter-in-place orders across the community due the particulate emissions that resulted. Analysts hired by the LACDPH dubbed this cluster of metals a “fingerprint” of intrusion into the homes from the blowout (15). The emissions of BTEX and other HAP compounds are of particular concern as even at levels below health benchmarks they have been linked to health effects, including neurological, respiratory, and developmental effects (16–18).

Among those who lived in affected communities, pregnant women were a particularly vulnerable population. Fetuses are more sensitive than adults to diverse environmental toxicants due to vulnerability of developmental, growth, and maturation processes (19, 20). Pollutants that previously have been linked to adverse birth outcomes include particulate matter, ozone, nitrogen dioxide, benzene, polychlorinated biphenyls (PCBs), PAHs, and metals (21–24). Furthermore, recent research has shown residential proximity to active oil and gas wells, which shares some of the same exposure profile as the Aliso blowout, is related to adverse birth outcomes, including low birthweight (LBW), preterm births, and birth defects (25–28). Studies around the world have also linked maternal air pollution exposure during pregnancy to higher infant mortality, preterm birth, lower birthweight, impaired lung development and respiratory disorders, and disruptions in immune system development (29). These adverse outcomes can exert continued health effects throughout the life course (30, 31).

Here, we investigate whether the disaster affected adverse birth outcomes in the community downwind of the leak site. Our principal hypothesis is that HAPs from the facility penetrated the downwind residential areas and exposed residents to higher levels of some health-active HAPs, including BTEX compounds, hexane, and particles enhanced with metals, all of which have been associated with adverse birth outcomes. We aim to assess whether pregnant women living in communities with exposure to the Aliso Canyon gas blowout experienced a higher prevalence of adverse birth outcomes [LBW, term low birthweight (TLBW), and preterm birth] than expected. We made use of California’s birth records and a quasiexperimental (difference-in-difference) modeling design, comparing the affected areas and unexposed control communities to uncover differences in adverse birth outcome prevalence before, during, and after the massive gas blowout.

RESULTS

Birth cohort and impact zone

For analysis, we acquired all California birth records from the Office of Vital Statistics from October 2010 to October 2019, which was post–economic recession and pre-COVID pandemic. This provided a statewide birth cohort (more than 4 million records) and three outcomes of interest: (i) LBW, infants with a birthweight of <2500 g; (ii) TLBW, infants with a gestational age at birth of ≥37 weeks and weighing <2500 g; and (iii) Preterm births, infants with a gestational age of <37 weeks.

We estimated exposure to the gas leak in terms of maternal residence (did the mother live in the impact zone) and timing of birth (did the pregnancy overlap with the blowout). For the impact zone, we considered a 10-km (6.21-mile) buffer south of the leak site (SS-25 well) and west of the 5/405 freeway (Fig. 1). The blown out well was in the Santa Susana Mountains, and with northerly prevailing winds during the months of active leaking (8), the mountain range created a natural barrier that prevented exposure to communities north of the well. There was also some indication that the exposure was strongest south and southwest from the well. The LACDPH Community Assessment for Public Health Emergency Response (CASPER) study, which surveyed residents during the blowout, reported a clear east to west trend in the proportion of households that reported smelling a “gas-like odor,” with a higher proportion of households in south and southwest reporting odors (32). Airborne monitoring done during several days of the blowout also showed the highest concentrations of pollutants in these areas (1). Therefore, to assess potential variability within the 10-km southern buffer (Fig. 1) and dose-response, we also split the buffer into thirds of equal size to assess whether the estimated effects varied from east to west and conducted analyses with expanding buffer distances from the well at ≤5 km (3.11 miles), 5 to 10 km (3.11 to 6.21 miles), and 10 to 15 km (6.21 to 9.23 miles).

Fig. 1. — (A) Map showing the SS-25 well and outward buffers considered at 5, 10, and 15 km. (B) Impact area buffer (red, 10-km S buffer clipped at I = 5/405) and the matched control communities at the census tract level (blue).

We then created a timing of birth categorical variable to indicate whether different periods of pregnancy (late, mid, and early) overlapped with the blowout (23 October 2015 to 18 February 2016), along with three 1-year periods before (23 October 2012 to 22 October 2015) and after the blowout (25 October 2016 to 24 October 2019), and a 2-year reference period (23 October 2010 to 22 October 2012). The 1-year periods before the blowout were used to assess parallel trends, which is discussed below. Further details are provided in Materials and Methods.

Women living in the impact zone whose pregnancy overlapped with the blowout were considered to have experienced blowout related exposure, whereas women living in the rest of the state who gave birth during the same periods were considered unexposed. The nature of the event, with a defined exposure period to a large population, allowed us to assess potential effects during the event (direct overlap periods) along with possible effects in the years after that might be attributable to longer-term exposures or persistent stress in the affected community.

We used a difference-in-difference approach, comparing the prevalence of the adverse birth outcomes before, during, and after the gas leak (33). The difference-in-difference design is a quasiexperimental approach that involves a well-defined study population and treatment groups, or in this case exposure groups, such that exposure and control groups are easily distinguished. However, the exposure is not randomly assigned, and the parallel trends assumption is therefore required for causal claims (34). The parallel trends assumption here means that we assume any trends in adverse birth outcomes across the blowout periods would have been similar in the exposed impact zone and the unexposed control communities if the blowout had not occurred (i.e., parallel trends over time).

To assess these effects in logistic regression models we included an interaction term between the indicators for maternal residence in the impact zone and the categorical date of the birth time variable. This term represents the ratio between the observed joint effect (maternal residence in the impact zone and each relevant pregnancy period overlapping with the blowout) and the expected joint effect. Under the assumption of parallel trends, this interaction term represents the excess adverse births attributable to the gas leak (see Statistical Analysis). To assess the validity of the parallel trends assumption, we compared the observed prevalence of the birth outcomes to the expected prevalence in the 3 years before the blowout as, during this period, the blowout exposures could not have been responsible for any differences, and thus we would expect similar trends across these periods.

For unexposed control communities, we used all births from three primary regions: (i) all of Los Angeles County, excluding the impact zone; (ii) matched communities (census tracts; Fig. 1B); and (iii) all statewide urban communities, excluding the impact zone. We also included sensitivity analyses with several other comparison communities to assess whether effects were robust across different comparison groups.

We have included a directed acyclic graph (DAG) in the Supplementary Materials to provide details of our conceptual model (fig. S1).

Descriptive statistics

Demographics of the birth cohort are shown in Table 1. Between 23 October 2010 and 24 October 2019, there were 1,056,901 births recorded in Los Angeles County (excluding the Aliso buffer) and 13,848 to mothers residing within the 10-km south impact zone. Over the 9-year period, the proportion of infants born LBW (6.3% versus 6.2%), TLBW (2.5% versus 2.5%), and preterm (8.4% versus 8.7%) was similar in the impact zone and the rest of LA County. Overall, there were 1527 babies born to mothers living in the impact zone whose pregnancy overlapped with the blowout at any point. Table S1 details the demographics of the birth cohort during the different time periods of interest. Table S2 details the sample size according to the birth outcomes for the affected and reference communities.

Table 1. Demographics of the birth cohort, including births from 23 October 2010 to 24 October 2019.

Limited to singletons, excluded implausible birthweight and gestation lengths, and those missing maternal zip code. General inclusion criteria: birthweight of >500 and <6800 g, birth week of >20 and <45, zip code provided, and singleton.

Covariate	Category	Exposed Aliso communities	Los Angeles County	Matched control communities (tracts)	Statewide urban
		10-km buffer south of SS-25	Excluding exposed	Excluding exposed	Excluding exposed
		n = 13,848	n = 1,056,901	n = 20,196	n = 3,812,896
		n (%) or means ± SD	n (%) or means ± SD	n (%) or means ± SD	n (%) or means ± SD
Maternal age	Means ± SD	31.1 ± 5.6	29.6 ± 6.2	31.0 ± 5.5	29.5 ± 6.1
	<20	349 (2.5)	57,658 (5.5)	488 (2.4)	198,914 (5.2)
	20–25	1933 (14.0)	230,905 (21.8)	2729 (13.5)	846,230 (22.2)
	26–30	3726 (26.9)	281,060 (26.6)	5732 (28.4)	1,059,926 (27.8)
	31–35	4784 (34.5)	294,809 (27.9)	7056 (34.9)	1,071,463 (28.1)
	36+	3056 (22.1)	192,461 (18.2)	4190 (20.7)	636,317 (16.7)
Maternal race/ethnicity	Hispanic, any race	5050 (36.5)	598,383 (56.6)	6215 (30.8)	1,812,731 (47.5)
	White, non-Hispanic	5003 (36.1)	197,266 (18.7)	10,923 (54.1)	1,027,095 (26.9)
	Black, non-Hispanic	602 (4.3)	77,199 (7.3)	209 (1.0)	220,158 (5.8)
	Asian/Pacific Islander, non-Hispanic	2905 (21)	165,169 (15.6)	2499 (12.4)	623,026 (16.3)
	Other/unknown	288 (2.1)	18,884 (1.8)	350 (1.7)	129,886 (3.4)
Maternal education	Mean of ordinal ranks (0–8)^*	5.1 ± 1.8	4.2 ± 2	5.0 ± 1.9	4.4 ± 2
Baby’s sex	Male	7101 (51.3)	542,231 (51.3)	10,439 (51.7)	1,954,758 (51.3)
Baby’s sex	Female	6747 (48.7)	514,663 (48.7)	9757 (48.3)	1,858,113 (48.7)
Parity	1st	6058 (43.7)	431,021 (40.8)	8236 (40.8)	1,527,460 (40.1)
	2nd	4908 (35.4)	337,616 (31.9)	7388 (36.6)	1,225,554 (32.1)
	3rd	1981 (14.3)	170,827 (16.2)	3131 (15.5)	624,840 (16.4)
	4+	901 (6.5)	117,437 (11.1)	1441 (7.1)	435,042 (11.4)
Prenatal care	No	23 (0.2)	3873 (0.4)	32 (0.2)	19,744 (0.5)
Prenatal care	Yes	13,825 (99.8)	1,053,028 (99.6)	20,164 (99.8)	3,793,152 (99.5)
Season of birth	Fall	3553 (25.7)	273,320 (25.9)	5192 (25.7)	974,949 (25.6)
	Winter	3418 (24.7)	252,749 (23.9)	5077 (25.1)	925,952 (24.3)
	Spring	3649 (26.4)	275,830 (26.1)	5126 (25.4)	999,264 (26.2)
	Summer	3228 (23.3)	255,002 (24.1)	4801 (23.8)	912,731 (23.9)
Adverse birth outcomes	LBW	868 (6.3)	65,890 (6.2)	1106 (5.5)	229,020 (6.0)
	TLBW	320 (2.5)	23,836 (2.5)	351 (1.9)	81,120 (2.3)
	Preterm	1162 (8.4)	92,253 (8.7)	1466 (7.3)	314,990 (8.3)

Open in a new tab

0, no formal education; 1, eight grade or less; 2, 9th grade through 12th grade–no diploma; 3, high school graduate or GED completed; 4, some college credit–no degree; 5, associate degree; 6, bachelor’s degree; 7, master’s degree; 8, doctorate or professional degree.

Estimated effects of the blowout on LBW outcomes

During the blowout, the prevalence of LBW and TLBW was higher than expected among women living in the affected area whose late pregnancy overlapped with the disaster.

Using the full 10-km southern buffer with the rest of LA County as the reference community, we observed the prevalence of LBW neonates was 44 to 50% higher than expected among women living in the impact zone whose last 12 weeks of pregnancy (~3rd trimester) overlapped with the blowout [2-year reference: odds ratio (OR) = 1.44, 95% confidence interval (CI) = 1.07, 1.95; table S3A; 4-year reference: OR = 1.50, 95% CI = 1.13, 1.99; table S4]. This effect was strongest among women living in the middle (OR = 1.64, 95% CI = 1.11, 2.41) and western (OR = 2.34, 95% CI = 1.10, 4.96) segments of the buffer, which was south and southwest of the well (Fig. 2 and table S4). Given the effect sizes but limited sample size, particularly in the western segment (n = 77 births during late pregnancy exposure period) and concerns about random fluctuations with small numbers, for further analysis, we combined the middle and western segments.

The observed and expected LBW prevalence across the 9-year period in middle and western segments is visualized in Fig. 2B. From October 2010 to October 2019, the prevalence of LBW remained mostly stable between 6.5 and 7% in the rest of LA County. Before the blowout, the prevalence of LBW in the affected communities and LA County were similar and the time trends were relatively parallel. During the blowout, however, the prevalence of LBW was 11.1% among pregnant women living in the affected communities during late pregnancy, which was 74% higher than expected (OR = 1.74; 95% CI = 1.20, 2.53). Following the blowout, the prevalence of LBW in the affected communities returned to expected levels.

These effects were robust across the different reference communities we considered. For the entire 10-km southern buffer, the interaction OR estimate ranged from 1.43 (95% CI = 1.03, 1.98) to 1.65 (95% CI = 1.08, 2.52) (table S3A) and 1.72 (95% CI = 1.17, 2.54) to 1.98 (95% CI = 1.23, 3.20) in just the middle and western segments (table S3B).

We further observed an increasing dose-response with distance from the well (Table 2). During the late pregnancy exposure period [date of birth (DOB): 23 October 2015 to 11 April 2016], the odds of having an LBW baby were 60 to 100% higher among women living ≤5 km from the well (full 10-km S buffer: OR = 1.61, 95% CI = 0.77, 3.36; middle/western segments: OR = 2.02, 95% CI = 0.91, 4.47) and 45 to 76% higher for those for those living between 5 and 10 km (full 10-km S buffer: OR = 1.45, 95% CI = 1.11, 1.90; middle/western segments: OR = 1.76, 95% CI = 1.25, 2.48) relative to the rest of LA County. After 10 km, the effect was greatly attenuated (full 10-km S buffer: OR = 1.15, 95% CI = 1.00, 1.33; middle/western segments: OR = 0.97, 95% CI = 0.78, 1.21).

Table 2. Dose-response of distance of maternal address from the well and LBW.

Effect of maternal residence within the impact zones on LBW, across varying distances from the leaking well SS-25 during the late pregnancy exposure period. All births across LA County are included as the reference. Late pregnancy exposure period: DOB: 23 October 2015 to 11 April 2016. REF, reference.

Maternal residence distance from SS-25	Full 10-km S buffer		Middle/Western segments
Maternal residence distance from SS-25	OR (95% CI)	P for trend	OR (95% CI)	P for trend
>15 km	REF	4.8 × 10⁻⁴	REF	7.8 × 10⁻³
10–15 km	1.15 (1.00, 1.33)		0.97 (0.78, 1.21)
5–10 km	1.45 (1.11, 1.90)		1.76 (1.25, 2.48)
<5 km	1.61 (0.77, 3.36)		2.02 (0.91, 4.47)

Open in a new tab

When limiting to exposure during the past month of pregnancy specifically, we observed similar results as when considering the 12-week period before birth, both for the entire 10-km southern buffer [(i) LA County reference: interaction OR = 1.50, 95% CI = 1.11, 2.02; (ii) matched communities (tracts): 1.97, 95% CI = 1.27, 3.05; (iii) statewide urban: 1.50, 95% CI = 1.12, 2.02] and the middle/western segments [(i) LA County reference: 1.80, 95% CI = 1.25, 2.58; (ii) matched communities (tracts): 2.34, 95% CI = 1.45, 3.80; (iii) statewide urban: 1.80, 95% CI = 1.15, 2.58; table S5]. We also observed similar results when restricting the late pregnancy period to those with ≥30 days of exposure (LA County reference: full 10-km southern buffer, OR = 1.50, 95% CI = 1.11, 2.02; middle/western segments, OR = 1.86, 95% CI = 1.11, 2.76; table S6) and splitting the late pregnancy exposure period in two to distinguish exposure during different stages of the blowout (full 10-km southern buffer, period 1: OR = 1.42, 95% CI = 0.95, 2.12; period 2: OR = 1.57, 95% CI = 1.08, 2.27; table S7).

We further evaluated birthweight as a continuous outcome with the same interaction term, using linear regression. After log-transforming birthweight and scaling to the SD, on average, neonates born to mothers living in the 10-km southern buffer whose last 12 weeks of pregnancy overlapped with the blowout had a lower birthweight than expected (LA County reference and log-transformed birthweight: interaction β = −0.07, 95% CI = −0.14, 0.01; table S8). The effect was strongest among lower birthweight infants (<3000 g), where those with mothers living in the impact zone whose late pregnancy overlapped with the blowout were on average more than 100 g lighter than expected (LA County reference and untransformed birthweight in grams: interaction β = −115.0 g, 95% CI = −181.12, −48.95; table S8). Both results were again stronger in the middle and western segments (interaction β = −0.10, 95% CI = −0.20, 0.00; <3000-g strata: interaction β = −145.4 g, 95% CI = −228.65, −62.17). The full model results can be found in table S8.

Similar results were observed for TLBW, with a higher prevalence of TLBW among women living in the affected communities with late pregnancy period exposure (Fig. 3A). For women who were living in the affected communities (10-km S buffer) and gave birth after late pregnancy exposure (12 weeks before birth overlapped with blowout), the prevalence of TLBW babies was 66% higher than expected (LA County reference: interaction OR = 1.66, 95% CI = 1.04, 2.63). This was again strongest in the middle and western segments (LA County reference: interaction OR = 1.95, 95% CI = 1.10, 3.46). Results were also robust across the different reference communities (table S9).

Fig. 3. — (A) Middle and West Aliso Buffer Segments Combined: Observed and expected TLBW prevalence using LA County as the reference. Results from the crude model are shown. (B) Middle and West Aliso Buffer Segments Combined: Observed and expected preterm birth prevalence using LA County as the reference. Results from the crude model are shown.

The predicted log odds from crude models can be found in figs. S2 to S4 to assess parallel trends. Before the blowout, relatively parallel trends were observed for both LBW and TLBW.

Estimated effects of the blowout on preterm birth

For preterm birth, we did not observe a clear pattern of parallel trends (Fig. 3B and fig. S5). Multiple periods before, during, and after the gas leak showed a higher prevalence of preterm births than expected (table S10). The most consistently associated periods were the year before the blowout (DOB: 23 October 2014 to 22 October 2015) and the year after (DOB: 25 October 2016 to 24 October 2017) in the middle/west segments. During both periods, the joint effect was more than 30% higher than expected (year prior, OR = 1.38, 95% CI = 1.04, 1.83; year after, OR = 1.32, 95% CI = 1.00, 1.75). For the late pregnancy period, which was associated with LBW and TLWB, the prevalence of preterm births was also 30% higher than expected (LA County Reference, OR = 1.30, 95% CI = 0.91, 1.87).

Estimated effects of the blowout on infant sex prevalence

We also assessed the sex prevalence among infants born to mother’s living in the Aliso communities. For pregnancies with first-trimester exposure, more male infants were born than expected. Before the blowout, the annual prevalence of male (50.5 to 51.9%) and female (48.1 to 49.5%) infants born in the Aliso communities was similar to the rest of LA County (male: 51.2 to 51.3%; female: 48.5 to 48.8%; table S1). During the blowout, however, the proportion of male infants increased from 51.9% (mothers with late pregnancy exposure) to 55.2% (mothers with early pregnancy exposure), whereas the proportion of female infants decreased from 48.1% (mothers with late pregnancy exposure) to 44.8% (mothers with early pregnancy exposure). Overall, the proportion of male infants born was >20% higher in the Aliso communities for those with early pregnancy exposure than expected (full 10-km south buffer: interaction OR = 1.22, 95% CI = 0.98, 1.52; middle/west segments: interaction OR = 1.27, 95% CI = 0.94, 1.71) based on LA County as the reference community (table S11).

Sensitivity analyses

For all three adverse birth outcomes, similar results were observed when using (i) a 4-year reference period, ensuring the reference period was not an abnormal year in terms of high or low adverse birth rates, and (ii) a matched reference time period to ensure we were comparing the exposure period of interest to similar calendar periods in prior years and (iii) when collapsing the three pregnancy overlap periods into one to ensure we were comparing similar time-period lengths (table S12). For the 1-year exposure period (23 October 2015 to 24 October 2016), the interaction effect estimates for the birthweight outcomes, while still statistically significant and elevated, were attenuated relative to the late pregnancy specific result, which is expected given that, for most analyses, the third-trimester period showed the most prominent increase in risk. In addition, for preterm birth, using the matched control communities (census tracts), there were 34% more preterm births than expected during the 1-year direct exposure period (23 October 2015 to 24 October 2016; interaction OR = 1.34, 95% CI = 1.01, 1.78). However, again, there was a period before the blowout period, which also showed >30% increases over expected, violating the parallel trends assumption (table S12).

Sensitivity analyses for models including additional covariates are shown in table S13. As expected, given the difference-in-difference design, the results were similar across all models, including crude models and adding a maximum number of covariates.

DISCUSSION

The Aliso Canyon gas blowout, which lasted 112 days, was the largest uncontrolled gas leak from an underground storage facility in the history of the United States, releasing an estimated 109,000 tons of methane (1, 2). At the time of the leak, roughly 200,000 residents were living in the impact zone south of the facility (fig. S5) and over 1500 births occurred within the 10-km southern buffer in the year after the event. To model adverse birth outcomes before, during, and after the event in the impact zones compared to unexposed areas, we used a quasiexperimental design using California’s birth records. We found that the prevalence of LBW and TLBW were 50 to 100% higher than expected among mother’s living in the affected areas whose late pregnancy periods overlapped with the blowout. Any pregnancy exposure was linked to a >30% excess of LBW. Under the assumption of parallel trends, which we observed in the 5 years before the leak, our results suggest that there was excess LBW and TLBW attributable to the gas leak.

Previous studies have shown that exposure to higher levels of different criteria air pollutants during pregnancy increase the risk of adverse birth outcomes and notably LWB. A recent systematic review found that exposure to PM_2.5 (particulate matter < 2.5 micrometers) or ozone was associated with LBW in 25 of 29 studies (86%) (35). Traffic markers, including nitric oxides (NO_x and NO₂), and proximity to air emissions from industrial facilities have also been reported to increase LBW risk (36). Although the Aliso blowout was quite unique in terms of magnitude and duration, there has also been recent research that shows residential proximity to active oil and gas wells is related to adverse birth outcomes. For instance, living near active wells was related to a 40% increase in LBW in California and residential proximity to natural gas development has been related to adverse birth outcomes (25–28). Although oil and gas development includes several sources of exposure and associated air pollutants, there are many overlapping exposures between the current study and some of these prior industrial source studies, including VOCs like the BTEX compounds, mercaptans, and heavy metals.

Birthweight is a measure of fetal growth and development. Although there is an ongoing debate about whether LBW is a disorder on its own or a proxy for developmental deficits or adverse impacts on pregnancy (37), LBW has been identified as a major risk factor contributing to the global disease burden (38). Multiple biologic mechanisms might be in play during pregnancy with air toxics that impact fetal growth, including systemic and placental inflammation, oxidative stress, altered maternal vascular, cardiac, or pulmonary function, and disrupted maternal and placental gene expression (35).

In sensitivity analysis, we observed the strongest effect estimates in regions directly south and southwest from the well. This is in line with what we know from pollutant measurements during the event, which indicated higher levels in southern and southwestern versus southeastern regions (14). Although with changing wind patterns over the 112 days, some level of exposure likely affected the entire surrounding region. Furthermore, measurement during the blowout in the area provided evidence for increases in ambient levels of several HAPs, including benzene, n-hexane, toluene, and styrene, along with methane and particulate matter (8). In addition to the air pollution literature, BTEX chemicals, for instance, from industrial facilities, have also widely been reported to increase the risk of LBW, with a systematic review determining that the link between benzene and LBW is strong (23, 36). Still, although these buffers are indicating areas of higher effect within the region, the delineation likely does not show exactly where increased risk starts and ends. There are likely gradual declines in risk outwardly from the blown out well, which our dose-response results support, with pregnant women living in the communities closest to the well showing the highest odds of LBW and decreasing effects observed as far out as 15 km. A similar pattern likely exists along the east-west axis as well.

We also observed the strongest effects on LBW with late pregnancy exposure. Fetal growth accelerates strongly in the last trimester, when most of the weight gain is observed (39). Late pregnancy is therefore a vulnerable time for growth restriction. LBW is due to either preterm birth, intrauterine growth restriction (IUGR), or both. Although IUGR can be initiated at any time during pregnancy, most IUGR is asymmetrical with features of malnutrition (70 to 80% of IUGR), which generally results from insults later in pregnancy (40, 41). The finding that LBW risk was most prominently increased with late pregnancy exposure could therefore be due to a combination of late pregnancy being a vulnerable time for IUGR and exposures contributing to fetal loss and livebirth bias with earlier pregnancy exposures, which is discussed below. In addition, the severity of the leak (tons of hydrocarbons released per hour) did vary over the blowout. It was highest in the first month of the leak and decreased over time. The emission rate peaked around 1 December 2015, at an estimated 50 tons of methane/hour and gradually fell to about 20 tons/hour before the plugging of the well (1). The correlation between VOC release and methane at different stages of the blowout, however, is uncertain. Thus, although the methane emissions were more intense earlier in the blowout, we do not know whether this was also the case for other blowout-related pollutants. In addition, several well-kill actions in the middle and later stages of the leak released particles, including toxic metals, which were measured in indoor dust samples in the surrounding communities (15). There was also evidence oily particles were released in the later stages of the leak, from indoor sampling, brown staining on cars and houses, and in our own particle monitoring (8–11). Thus, we cannot accurately assess whether the early, middle, or later periods of the leak emitted more toxic material that could have adversely affected the pregnancies. In our sensitivity analysis, however, we observed similar effect estimates when splitting the late pregnancy exposure period into two periods to differentiate late pregnancy exposure in the first half of the blowout and late pregnancy in the second half of the blowout, suggesting that, across the blowout, late pregnancy was a vulnerable period of pregnancy for exposure.

For preterm birth, there was some suggestion of excess events during the blowout exposure periods. Estimates during the late pregnancy exposure period were generally elevated by about 30% in most models and up to 47% for the overall pregnancy exposure overlap period. The prevalence trajectories, however, did not suggest parallel trends. There was also a period before the gas leak during which risk was increased. Furthermore, the year after the overall pregnancy overlap period, which encompassed women who conceived as the leak was ending through 2016. During this period, preterm births were 30 to 40% more prevalent than expected. It is possible that this increase is related to residual exposure effects. Heavy metal residues were measured in homes in the months after the leak was plugged and metals are known to persist in the environment (15). There may also have been indirect exposure effects due to continuous stress related to the event and ongoing operation of the facility. However, given the period directly before the blowout that also showed elevated preterm births, violating the parallel trend assumption, we cannot conclude that the observed preterm effects are due to the blowout.

Last, we also observed a higher-than-expected proportion of male infants and lower proportion of female infants born to women living in the Aliso communities whose early pregnancy overlapped with the blowout. During this early pregnancy exposure period, the proportion of male infants born was over 20% higher than expected. The proportion of male and female infants at birth is generally remarkably consistent, with slightly more male infants born in a population (42). Therefore, changes in these proportions may suggest increased levels of miscarriage and embryonic loss. Periods of stress, natural disasters, and different pollutants among other factors have all been related to changes in the male to female sex ratio at birth and may reflect exposure-related loss (43, 44). Although some studies have found a decline in the male to female sex ratio at birth, suggesting that male fetuses are preferentially lost, others saw more male and less female infants born. For instance, more male infants were born after Hurricane Katrina than expected (45). A large study conducted in the US and Sweden also suggested that heavy metal exposures may contribute to a higher male to female birth ratio (44). Our sex results here suggest that exposure to the Aliso blowout may have resulted in early pregnancy miscarriage that distorted to male to female ratio at birth. They also suggest the possibility of a live birth bias occurring for our birth outcome analyses, that is, exposure early in pregnancy leading to fetal loss selectively preventing the most vulnerable fetuses from surviving (46). Given that exposure, pregnancy complications, and growth restrictions are positively related to fetal loss, the resulting live birth selection would bias the results toward the null, suggesting we are underestimating the true effect sizes. This relationship is shown in our DAG in the Supplementary Materials.

Notable strengths of this study include the quasiexperimental nature of the event, with a well-documented exposure period. With this information, we were able to use California’s birth records to determine adverse birth outcome odds for the entire California birth cohort and specifically for pregnant women with residential birth record addresses in the impact zone. Together with birth timing, this allowed us to identify women who had likely experienced exposure to the gas leak during different stages of pregnancy. The quasiexperimental nature also limits concerns about confounding due to non–time-varying factors beyond those we were able to control for. Still, there are several limitations that should be noted. First, maternal residential location during pregnancy was based on the maternal address provided on the birth certificate. This may introduce misclassification. For instance, if a pregnant woman living in the affected region left her home before giving birth, exposure may be misclassified as we have to assume the address on the birth certificate represents the pregnancy residence. Thousands of residents also evacuated the area during the leak, which could have led to additional exposure measurement error. Lacking the necessary information, we were unable to consider residential mobility. Such exposure misclassification as well as selection bias due to women moving out of the state before giving birth, however, would be expected to bias results toward the null in many plausible scenarios. For instance, exposure (living in the affected communities) and pregnancy complications (e.g., a prenatal ultrasound suggesting small for gestational age) may both have contributed to the decision to move away from the Aliso area during the blowout and thus resulted in high-risk exposed pregnancies being included among the unexposed or missed entirely if they moved out of California, leading to an underestimation of the effect size. Though it should be noted that other scenarios of differential exposure misclassification may result in bias toward or away from the null depending on who is misclassified. In addition, nondifferential exposure misclassification may also arise from missing or incorrect data on birth certificates, which is again most likely moving effect estimates toward the null. Together, this, along with the live birth bias, suggests that our results may be an underestimate of the true effect sizes.

Furthermore, with the assumed impact area, we were unable to delineate whether the observed effects were due to air pollution exposures or from the psychosocial stress generated by the disaster. Other disasters, such as hurricanes (47), have also been associated with adverse birth outcomes. With this study design, we are unable to determine whether the adverse effects resulted from pollution exposures, maternal stress, or some combination of both. We did, however, observe both a dose-response gradient with distance from the well and stronger effects in the south and southwest regions than those in the southeast region, where observed benzene and methane levels were lower than in the other regions (14). However, women living within 10 km and in the southeast region likely also knew of the event and thus were just as likely to experience maternal stress as those in the other regions proximal to the well. The LACDPH CASPER study, which interviewed residents during the blowout, showed that the proportion of households who reported feeling stressed was relatively consistent across the southern semicircle buffer (>25% of households across the buffer, with no east to west trend) (32). The proportion of households, however, that reported smelling an odor showed a clear east to west trend, with the lowest proportion in the eastern segment of the buffer (0 to 25%) and highest in the western segment (50 to 75%) (32). Thus, this east to west gradient in the LBW effect that we observed along with the CASPER study results lends credence to the conclusion that air pollution exposure itself influenced the birth outcomes. We are now preparing comprehensive, highly resolved air pollution exposure estimates that will enable us to use mediation analysis to parse out the likely proportion of the observed effect attributable to direct air pollution exposures in future research.

Overall, this study provides evidence that the prevalence of LBW and TLBW was up to twofold higher than expected among mothers living in the Aliso Canyon affected areas who were in the later stages of pregnancy during the blowout event, which is likely attributable to the Aliso Canyon disaster.

MATERIALS AND METHODS

Research protocols were approved by the UCLA Institutional Review Board (IRB-23-0129) and ICMJE guidelines followed.

Birth data and outcomes

We acquired all California birth records from the Office of Vital Statistics from October 2010 to October 2019. This provided a statewide birth cohort for analysis (more than 4 million records). Gestational age at birth (in weeks) was derived from birth record information, i.e., the difference between the date last menses began and the DOB. We excluded records with extreme or implausible birthweights (<500 or >6800 g) and gestational ages (<20 or >45 weeks), multiple births, or missing the maternal residential address. For the remaining birth records (n = 4.28 million), we defined three outcomes:

1) LBW, infants with a birthweight of <2500 g; normal birthweight (NBW), 2500 to <4000 g; and high birthweight (HBW), ≥4000 g

2) TLBW, infants with a gestational age at birth of ≥37 weeks and weighing <2500 g

3) Preterm birth with a gestational age of <37 weeks, term (37- to 41-week gestation), and postterm (≥42 weeks) births

Exposure assessment

Exposure to the gas leak was estimated in terms of maternal residence (did the mother live in the impact zone) and timing of birth (did the pregnancy overlap with the blowout).

For the impact zone, we initially considered a 6.21-mile (10-km) buffer south of the leak site (SS-25 well, latitude: 34.3150827778, longitude: −118.5640686111) and west of the 5/405 freeway (Fig. 1). We limited the impact zone to the south of the SS-25 well, the site of the blowout, for several primary reasons: (i) The well was in Santa Susana Mountains with northerly prevailing winds during the months of active leak. Table S2 in the following reference provides a wind rose diagram from a meteorological station near the well that demonstrates this (8). (ii) The general lack of residential population to the north as the area is largely uninhabited due to the presence of the Santa Susana Mountains and various open spaces and parks. The nearest residential area to the north and west of the I-5 highway is Stevenson Ranch (distance 6.8 km to the southern section of the subdivision and 8.5 km to the middle of the subdivision). (iii) The natural barrier of the mountains likely decreased dispersion, particularly for methane, which is negatively buoyant, but also for the HAPs that were highly correlated with the CH₄ plume. (iv) Some of the remote sensing retrievals of methane available during the event showed that, when the wind was from the south, the plume was much smaller in size than when the faster dominant winds from the north directed the plume to the south of the leak (48). Thus, we anticipate negligible impacts from the blowout on populations to the North, so we excluded areas to the north from the impact zone.

To assess potential variability within the 10-km south buffer and potential dose-response, we also split the buffer into equal sized thirds to assess whether the estimated effects varied from east to west and conducted analyses with expanding buffer distances from the well at ≤5 km (3.11 miles), 5 to 10 km (3.11 to 6.21 miles), and 10 to 15 km (6.21 to 9.23 miles).

After the exclusions detailed above, between 23 October 2010 and 24 October 2019, there were 1,056,901 births recorded in Los Angeles County (excluding the Aliso buffer) and 13,848 to mothers residing within the full 10-km S impact zone.

We then created a timing of birth categorical variable to indicate whether different periods of pregnancy overlapped with the blowout (23 October 2015 to 18 February 2016). Specifically, we defined three periods of overlap and seven control periods as follows:

1) Late pregnancy overlap period: DOB between 23 October 2015 and 11 April 2016. This classified births where the mother’s last 12 weeks of pregnancy (~3rd trimester) overlapped with the blowout, including all births that took place during the blowout and those to mothers with at least 30 days of overlap in the 12-week period before birth.

2) Mid-pregnancy overlap period: DOB between 12 April 2016 and 1 August 2016. All births where the mother’s pregnancy overlapped with the blowout during the ~2nd trimester (13 to 28 weeks before birth) for ≥30 days but <30 days in the late pregnancy period.

3) Early pregnancy overlap period: DOB between 2 August 2016 and 24 October 2016. All births where the mother’s pregnancy overlapped with the blowout during the ~1st trimester (29 to 40 weeks before birth) for ≥30 days but <30 days in the mid-pregnancy period.

We further categorized each of the 3 years before (23 October 2012 to 22 October 2015) and the 3 years after (25 October 2016 to 24 October 2019) the blowout to assess prevalence trends and then set a 2-year reference period (23 October 2010 to 22 October 2012). Women living in the affected communities with pregnancy period overlap were considered to have experienced blowout related exposure, whereas women living in the rest of the state who gave birth during the same periods were considered unexposed. This was included as a categorical term in the model, so each category was compared separately to the reference. We included all births during the blowout, even those during the first month where exposure would be <30 days for two reasons: (i) the likelihood that the blowout was happening before the report and (ii) that exposures in the few weeks before birth can be quite relevant for growth restriction and preterm birth.

We also created three alternative time variables for sensitivity analyses. We generated (i) an exposure period spanning 12 months by combining the three pregnancy overlap periods 1 to 3 above (DOB: 23 October 2015 to 24 October 2016). This approach no longer delineates exposures by trimester but instead generates comparable length periods of ~1-year duration. (ii) A 4-year reference period (23 October 2010 to 22 October 2014) with the trimester specific categories described above to ensure the reference period was not an abnormal year with higher or lower adverse birth rates. (iii) A reference period from 2010 to 2014 matched the exact calendar time of the first exposure period (23 October 2015 to 11 April 2016). This ensures that the pregnancies considered exposed during the main exposure blowout period are compared to same season pregnancies in the years before the blowout. (iv) Restricting the late pregnancy exposure period to only those with ≥30 days of exposure analogous to the mid and early exposure groups. (v) Splitting the late pregnancy exposure period in two to differentiate exposures in the first half and second half of the blowout, given that there was known attenuation in the intensity of methane exposure before the well plug.

Statistical analysis

For analysis, we used a quasiexperimental approach, difference-in-difference, comparing the prevalence of the adverse birth outcomes before, during, and after the gas leak with difference-in-difference models (33). This approach makes use of the longitudinal birth cohort data from exposed and control communities, before, during and after the leak, to obtain an estimate of the difference in the observed prevalence of adverse birth outcomes and what is expected, providing an estimate of the impact of the gas blowout on the birth outcomes. To accomplish this, we used birth outcomes in the same time periods from mothers who lived in unexposed communities to estimate the counterfactual adverse birth outcome prevalence in affected communities, i.e., the outcome prevalence we would have expected if the gas leak had not occurred.

We used all births from three primary unexposed control communities: (i) all of Los Angeles County, excluding the affected area; (ii) matched communities (census tracts); and (iii) all statewide urban communities, excluding the affected area. Matched communities from nearby regions in Southern California were selected on the basis of distinctive demographic and household characteristics of people living within three zip codes (91311, 91326, and 91344) 8 km south of the blowout in ESRI Community Analyst. We assessed race (% White alone and % Asian alone, which, combined, comprise an average 73% of residents in those zip codes), average household income, dwelling type (% homes single-detached unit), and home ownership (% homes owner-occupied) from American Community Survey 5-year averages for 2020 (49). A value within 10 to 20% of the combined average value of the three affected zip codes for each demographic and housing characteristic was then used to program the ESRI Community Analyst Smart Search function to screen for census tracts in southern California with similar characteristics in all five categories above the following minimum thresholds: White alone > 45%; Asian alone > 15%; average income > $126,000; single-detached unit > 65%, and owner-occupied housing > 49%. We later compared communities on age distribution. Using CalEnviroScreen version 4.0 (50), we compared affected and matched communities on ozone, PM_2.5, diesel particulate matter, and traffic. We only excluded matches that were within 5 miles (8.05 km) of the Pacific Coast due to their unique environmental and air quality conditions. A total of 56 census tracts, primarily from the neighboring Ventura County (Fig. 1B), were selected for reference comparisons.

In sensitivity analyses, we also assessed the impact of using additional reference communities as the unexposed control population, including all births from (i) the San Fernando Valley, excluding the affected area, and (ii) neighboring communities, ≤10 miles (16.09 km) from SS-25, excluding the affected area. We included the additional comparison communities in the sensitivity analysis to assess whether the effects were robust across different comparison groups.

We used logistic regression, modeling each birth outcome separately [LBW versus NBW, TLWB versus term normal birthweight (TNBW), and preterm versus term]. We included an interaction term between the indicators for maternal residence in the impact zone and the categorical time of birth variable. This term represents the ratio between the observed joint effect (maternal residence in the impact zone and each relevant pregnancy period overlapping with the blowout) and the expected joint effect based on the main effects of the exposure space and DOB time. Under the assumption of parallel trends, the interaction term represents the excess adverse births attributable to the gas leak. Parallel trends assume that, if the gas leak did not occur, the trend in the outcome prevalence across the blowout periods would be similar in the affected communities to the reference communities. For logistic regression, this trajectory is assessed on the log-odds scale. We evaluated parallel trends graphically using 1-year time periods in the 5 years before the blowout. Birthweight was also assessed as a continuous variable using linear regression with the same interaction term outlined.

We further included maternal age (<20, 20 to 25, 26 to 30, 31 to 35, and 36+), maternal education, prenatal care (yes/no), parity (1st, 2nd, and 3+), sex, maternal race/ethnicity [white (non-Hispanic), Hispanic (any race), black, Asian/Pacific Islander, and other/unknown], and season of birth as covariates in the models.

In addition, the California Communities Environmental Health Screening Tool (CalEnviron) developed by CalEPA integrates data on environmental, public health, and socioeconomic conditions in California’s 8000 census tracts to provide estimates of pollution burdens and vulnerabilities in communities throughout the state (50). In sensitivity analyses, we assessed models including additional covariates: birth year, which was not included in the primary models due to colinearity concerns with the DOB categorical variable; the overall CalEnviron Score (CES_Score4, version 4.0); CalEnviron subscores for poverty, unemployment, and education specifically; and CalEnviron subscores for PM_2.5, traffic, and pesticides.

Acknowledgments

Funding: Funding was provided in full by the Los Angeles County Department of Public Health and secured through a consent decree agreement between the County of Los Angeles, County Counsel for the County of Los Angeles, the Los Angeles City Attorney, the California Attorney General, and the California Air Resources Board with SoCal Gas. The contents do |not represent the official views or policies of the State, County, or City.

Author contributions: Conceptualization: K.C.P., M.J., H.L., J.M., M.C., B.R., and S.B. Data curation: K.C.P., S.M., J.M., Q.M., Y.Y., and S.B. Formal analysis: K.C.P., M.J., H.L., J.M., M.C., Y.Y., and S.B. Methodology: K.C.P., M.J., H.L., M.S., J.M., M.C., B.R., and S.B. Validation: K.C.P., M.J., S.M., M.S., J.M., B.R., and S.B. Visualization: K.C.P., M.J., H.L., J.M., D.E., C.B., and S.B. Resources: K.C.P., M.C., C.B., B.R., Y.Y., and S.B. Supervision: K.C.P., H.L., J.M., B.R., and S.B. Investigation: M.J., Q.M., and S.B. Software: K.C.P., J.M., and S.B. Project administration: K.C.P., H.L., C.B., B.R., and S.B. Funding acquisition: K.C.P., M.J., H.L., D.G.-G., D.E., and S.B. Writing—original draft: K.C.P., M.J., J.M., D.G.-G., and S.B. Writing—review and editing: K.C.P, M.J., S.M., H.L., M.S., J.M., D.G.-G., D.E., Q.M., M.C., C.B., B.R., Y.Y., and S.B.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All analysis is based on California’s Birth Records, which is available for research from the Office of Vital Records/Department of Public Health after appropriate approvals from the State of California and Institutional Review Boards. Access to this data must be through request from California’s Department of Public Health. For more information related to California birth record data access, see https://cdph.ca.gov/Programs/CHSI/Pages/Data-Applications.aspx# or contact the Health Information and Research Section (HIRS) at HIRS@cdph.ca.gov. Analysis code and results output are available on Zenodo (DOI: 10.5281/zenodo.16001909).

Supplementary Materials

The PDF file includes:

Figs. S1 to S5

Legends for tables S1 to S13

sciadv.adr6684_sm.pdf^{(2.2MB, pdf)}

Other Supplementary Material for this manuscript includes the following:

Tables S1 to S13

sciadv.adr6684_tables_s1_to_s13.zip^{(110.3KB, zip)}

REFERENCES AND NOTES

1.Conley S., Franco G., Faloona I., Blake D. R., Peischl J., Ryerson T. B., Methane emissions from the 2015 Aliso Canyon blowout in Los Angeles, CA. Science 351, 1317–1320 (2016). [DOI] [PubMed] [Google Scholar]
2.Los Angeles County Department of Public Health, Aliso Canyon Gas Well Blowout & Public Health Disaster, 2015-2016, http://publichealth.lacounty.gov/eh/healthresearch/frequently-asked-questions.htm.
3.H. K. Trabish, “Historic Los Angeles methane leak puts natural gas emissions under scrutiny,” Utility Dive, 21 December 2015; https://utilitydive.com/news/historic-los-angeles-methane-leak-puts-natural-gas-emissions-under-scrutiny/411060/.
4.J. Worland, H. Jones, “This Is How Much the California Gas Leak Is Hurting the Planet,” Time Magazine, 14 January 2016; https://time.com/4180692/california-methane-gas-leak-environment/?xid=homepage.
5.H. Blume, C. Rivera, “LAUSD works to close two Porter Ranch schools over gas leak,” Los Angeles Times, 16 December 2015; https://latimes.com/local/education/la-me-1217-porter-ranch-schools-20151217-story.html.
6.B. Brown, “Seven Years Since Aliso Canyon Gas Blowout: What’s Happened?,” NBC News Los Angeles, 1 Novemeber 2022; https://nbclosangeles.com/news/local/seven-years-since-aliso-canyon-gas-blowout-whats-happened/3022395/.
7.Los Angeles County Department of Public Health, Aliso Canyon Disaster Background, http://publichealth.lacounty.gov/eh/healthresearch/background.htm.
8.Garcia-Gonzales D. A., Popoola O., Bright V. B., Paulson S. E., Wang Y., Jones R. L., Jerrett M., Associations among particulate matter, hazardous air pollutants and methane emissions from the Aliso Canyon natural gas storage facility during the 2015 blowout. Environ. Int. 132, 104855 (2019). [DOI] [PubMed] [Google Scholar]
9.A. Walton, “Porter Ranch residents face an oily problem as they return home,” Los Angeles Times, 10 March 2016.
10.Air Quality Management District (AQMD), Ambient Air Analysis, http://aqmd.gov/home/news-events/community-investigations/aliso-canyon-update/air-sampling.
11.G. Wilcox, “Oily mist released at site of leaking gas well in Porter Ranch,” Los Angeles Daily News, 28 August 2017.
12.Los Angeles County Department of Public Health, “Aliso Canyon Natural Gas Disaster Air Quality Monitoring and Modeling Technical Report” (2018); http://publichealth.lacounty.gov/media/docs/AlisoCanyonAirMonitoring-ModelingReport.pdf.
13.Los Angeles County Department of Public Health, “Aliso Canyon Gas Leak Results from Expanded Air Monitoring” (2016); http://publichealth.lacounty.gov/media/docs/GLAirMonitoring.pdf.
14.South Coast Air Quality Management District, “Aliso Canyon Natural Gas Leak: Air Monitoring Results” (2018).
15.Los Angeles Department of Public Health, “Aliso Canyon Gas Leak Public Health Assessment” (2016); http://publichealth.lacounty.gov/eh/docs/healthresearch/aliso-canyon-gas-leak-public-health-assessment.pdf.
16.Garcia-Gonzales D. A., Shonkoff S. B. C., Hays J., Jerrett M., Hazardous air pollutants associated with upstream oil and natural gas development: A critical synthesis of current peer-reviewed literature. Annu. Rev. Public. Health 40, 283–304 (2019). [DOI] [PubMed] [Google Scholar]
17.Manisalidis I., Stavropoulou E., Stavropoulos A., Bezirtzoglou E., Environmental and health impacts of air pollution: A review. Front. Public Health 8, 14 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kelly F. J., Fussell J. C., Air pollution and public health: emerging hazards and improved understanding of risk. Environ. Geochem. Health 37, 631–649 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Zheng T., Zhang J., Sommer K., Bassig B. A., Zhang X., Braun J., Xu S., Boyle P., Zhang B., Shi K., Buka S., Liu S., Li Y., Qian Z., Dai M., Romano M., Zou A., Kelsey K., Effects of environmental exposures on fetal and childhood growth trajectories. Ann. Glob. Health 82, 41–99 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Mattison D. R., Environmental exposures and development. Curr. Opin. Pediatr. 22, 208–218 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Issah I., Duah M. S., Arko-Mensah J., Bawua S. A., Agyekum T. P., Fobil J. N., Exposure to metal mixtures and adverse pregnancy and birth outcomes: A systematic review. Sci. Total. Environ. 908, 168380 (2024). [DOI] [PubMed] [Google Scholar]
22.Govarts E., Nieuwenhuijsen M., Schoeters G., Ballester F., Bloemen K., de Boer M., Chevrier C., Eggesbø M., Guxens M., Krämer U., Legler J., Martínez D., Palkovicova L., Patelarou E., Ranft U., Rautio A., Petersen M. S., Slama R., Stigum H., Toft G., Trnovec T., Vandentorren S., Weihe P., Kuperus N. W., Wilhelm M., Wittsiepe J., Bonde J. P., OBELIX/ENRIECO , Birth weight and prenatal exposure to polychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (DDE): A meta-analysis within 12 European birth cohorts. Environ Health Perspect. 120, 162–170 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Veber T., Dahal U., Lang K., Orru K., Orru H., Industrial air pollution leads to adverse birth outcomes: A systematized review of different exposure metrics and health effects in newborns. Public Health Rev. 43, 1604775 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Guo L. Q., Chen Y., Mi B. B., Dang S. N., Zhao D. D., Liu R., Wang H. L., Yan H., Ambient air pollution and adverse birth outcomes: A systematic review and meta-analysis. J. Zhejiang Univ. Sci. B 20, 238–252 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
25.Tran K. V., Casey J. A., Cushing L. J., Morello-Frosch R., Residential proximity to oil and gas development and birth outcomes in California: A retrospective cohort study of 2006–2015 births. Environ. Health Perspect. 128, 67001 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Casey J. A., Savitz D. A., Rasmussen S. G., Ogburn E. L., Pollak J., Mercer D. G., Schwartz B. S., Unconventional natural gas development and birth outcomes in Pennsylvania, USA. Epidemiology 27, 163–172 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Gaughan C., Sorrentino K. M., Liew Z., Johnson N. P., Clark C. J., Soriano M., Plano J., Plata D. L., Saiers J. E., Deziel N. C., Residential proximity to unconventional oil and gas development and birth defects in Ohio. Environ. Res. 229, 115937 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
28.McKenzie L. M., Guo R., Witter R. Z., Savitz D. A., Newman L. S., Adgate J. L., Birth outcomes and maternal residential proximity to natural gas development in rural Colorado. Environ. Health Perspect. 122, 412–417 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Proietti E., Röösli M., Frey U., Latzin P., Air pollution during pregnancy and neonatal outcome: A review. J. Aerosol Med. Pulm. Drug Deliv. 26, 9–23 (2013). [DOI] [PubMed] [Google Scholar]
30.Power C., Kuh D., Morton S., From developmental origins of adult disease to life course research on adult disease and aging: Insights from birth cohort studies. Annu. Rev. Public Health 34, 7–28 (2013). [DOI] [PubMed] [Google Scholar]
31.Crump C., An overview of adult health outcomes after preterm birth. Early Hum. Dev. 150, 105187 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Los Angeles County Department of Public Health, “Aliso Canyon Gas Leak Community Assessment for Public Health Emergency Response (CASPER)” (2016); http://publichealth.lacounty.gov/media/docs/CASPERFinalReport.pdf.
33.Nianogo R. A., Benmarhnia T., O’Neill S., A comparison of quasi-experimental methods with data before and after an intervention: An introduction for epidemiologists and a simulation study. Int. J. Epidemiol. 52, 1522–1533 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Wing C., Simon K., Bello-Gomez R. A., Designing difference in difference studies: Best practices for public health policy research. Annu. Rev. Public Health 39, 453–469 (2018). [DOI] [PubMed] [Google Scholar]
35.Bekkar B., Pacheco S., Basu R., Basu R., Denicola N., Association of air pollution and heat exposure with preterm birth, low birth weight, and stillbirth in the US: A systematic review. JAMA Netw. Open 3, e208243 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Gong X., Lin Y., Bell M. L., Zhan F. B., Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA. Environ. Int. 120, 181–198 (2018). [DOI] [PubMed] [Google Scholar]
37.Hertz-Picciotto I., Commentary: When brilliant insights lead astray. Int. J. Epidemiol. 30, 1243–1244 (2001). [DOI] [PubMed] [Google Scholar]
38.Blencowe H., Krasevec J., de Onis M., Black R. E., An X., Stevens G. A., Borghi E., Hayashi C., Estevez D., Cegolon L., Shiekh S., Ponce Hardy V., Lawn J. E., Cousens S., National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: A systematic analysis. Lancet Glob. Health 7, e849–e860 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Mayo Clinic Staff, Pregnancy week by week (Mayo Clinic, 2022); https://mayoclinic.org/healthy-lifestyle/pregnancy-week-by-week/in-depth/fetal-development/art-20045997.
40.Sharma D., Shastri S., Sharma P., Intrauterine growth restriction: Antenatal and postnatal aspects. Clin. Med. Insights Pediatr. 10, 67–83 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Cutland C. L., Lackritz E. M., Mallett-Moore T., Bardají A., Chandrasekaran R., Lahariya C., Nisar M. I., Tapia M. D., Pathirana J., Kochhar S., Muñoz F. M., Low birth weight: Case definition & guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine 35, 6492–6500 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
42.H. Ritchie, M. Roser, Gender Ratio (Our World in Data, 2019); https://ourworldindata.org/gender-ratio.
43.Fontanesi L., Verrocchio M. C., D’Ettorre M., Prete G., Ceravolo F., Marchetti D., The impact of catastrophic events on the sex ratio at birth: A systematic review. Am. J. Hum. Biol. 36, e24003 (2024). [DOI] [PubMed] [Google Scholar]
44.Long Y., Chen Q., Larsson H., Rzhetsky A., Observable variations in human sex ratio at birth. PLOS Comput. Biol. 17, e1009586 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Grech V., Scherb H., Hurricane Katrina: Influence on the male-to-female birth ratio. Med. Princ. Pract. 24, 477–485 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Leung M., Kioumourtzoglou M. A., Raz R., Weisskopf M. G., Bias due to selection on live births in studies of environmental exposures during pregnancy: A simulation study. Environ. Health Perspect. 129, 47001 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Harville E. W., Pan K., Beitsch L., Sherchan S. P., Gonsoroski E., Uejio C., Lichtveld M. Y., Hurricane Michael and adverse birth outcomes in the Florida panhandle: Analysis of vital statistics data. Disaster Med. Public Health Prep. 17, e94 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Thompson D. R., Thorpe A. K., Frankenberg C., Green R. O., Duren R., Guanter L., Hollstein A., Middleton E., Ong L., Ungar S., Space-based remote imaging spectroscopy of the Aliso Canyon CH₄ superemitter. Geophys. Res. Lett. 43, 6571–6578 (2016). [Google Scholar]
49.United State Census Bureau, American Community Survey 5-Year Data (2009-2022) (2023); https://census.gov/data/developers/data-sets/acs-5year.2020.html#list-tab-1806015614.
50.OEHHA, CalEnviroScreen 4.0 (2023); https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figs. S1 to S5

Legends for tables S1 to S13

sciadv.adr6684_sm.pdf^{(2.2MB, pdf)}

Tables S1 to S13

sciadv.adr6684_tables_s1_to_s13.zip^{(110.3KB, zip)}

[R1] 1.Conley S., Franco G., Faloona I., Blake D. R., Peischl J., Ryerson T. B., Methane emissions from the 2015 Aliso Canyon blowout in Los Angeles, CA. Science 351, 1317–1320 (2016). [DOI] [PubMed] [Google Scholar]

[R2] 2.Los Angeles County Department of Public Health, Aliso Canyon Gas Well Blowout & Public Health Disaster, 2015-2016, http://publichealth.lacounty.gov/eh/healthresearch/frequently-asked-questions.htm.

[R3] 3.H. K. Trabish, “Historic Los Angeles methane leak puts natural gas emissions under scrutiny,” Utility Dive, 21 December 2015; https://utilitydive.com/news/historic-los-angeles-methane-leak-puts-natural-gas-emissions-under-scrutiny/411060/.

[R4] 4.J. Worland, H. Jones, “This Is How Much the California Gas Leak Is Hurting the Planet,” Time Magazine, 14 January 2016; https://time.com/4180692/california-methane-gas-leak-environment/?xid=homepage.

[R5] 5.H. Blume, C. Rivera, “LAUSD works to close two Porter Ranch schools over gas leak,” Los Angeles Times, 16 December 2015; https://latimes.com/local/education/la-me-1217-porter-ranch-schools-20151217-story.html.

[R6] 6.B. Brown, “Seven Years Since Aliso Canyon Gas Blowout: What’s Happened?,” NBC News Los Angeles, 1 Novemeber 2022; https://nbclosangeles.com/news/local/seven-years-since-aliso-canyon-gas-blowout-whats-happened/3022395/.

[R7] 7.Los Angeles County Department of Public Health, Aliso Canyon Disaster Background, http://publichealth.lacounty.gov/eh/healthresearch/background.htm.

[R8] 8.Garcia-Gonzales D. A., Popoola O., Bright V. B., Paulson S. E., Wang Y., Jones R. L., Jerrett M., Associations among particulate matter, hazardous air pollutants and methane emissions from the Aliso Canyon natural gas storage facility during the 2015 blowout. Environ. Int. 132, 104855 (2019). [DOI] [PubMed] [Google Scholar]

[R9] 9.A. Walton, “Porter Ranch residents face an oily problem as they return home,” Los Angeles Times, 10 March 2016.

[R10] 10.Air Quality Management District (AQMD), Ambient Air Analysis, http://aqmd.gov/home/news-events/community-investigations/aliso-canyon-update/air-sampling.

[R11] 11.G. Wilcox, “Oily mist released at site of leaking gas well in Porter Ranch,” Los Angeles Daily News, 28 August 2017.

[R12] 12.Los Angeles County Department of Public Health, “Aliso Canyon Natural Gas Disaster Air Quality Monitoring and Modeling Technical Report” (2018); http://publichealth.lacounty.gov/media/docs/AlisoCanyonAirMonitoring-ModelingReport.pdf.

[R13] 13.Los Angeles County Department of Public Health, “Aliso Canyon Gas Leak Results from Expanded Air Monitoring” (2016); http://publichealth.lacounty.gov/media/docs/GLAirMonitoring.pdf.

[R14] 14.South Coast Air Quality Management District, “Aliso Canyon Natural Gas Leak: Air Monitoring Results” (2018).

[R15] 15.Los Angeles Department of Public Health, “Aliso Canyon Gas Leak Public Health Assessment” (2016); http://publichealth.lacounty.gov/eh/docs/healthresearch/aliso-canyon-gas-leak-public-health-assessment.pdf.

[R16] 16.Garcia-Gonzales D. A., Shonkoff S. B. C., Hays J., Jerrett M., Hazardous air pollutants associated with upstream oil and natural gas development: A critical synthesis of current peer-reviewed literature. Annu. Rev. Public. Health 40, 283–304 (2019). [DOI] [PubMed] [Google Scholar]

[R17] 17.Manisalidis I., Stavropoulou E., Stavropoulos A., Bezirtzoglou E., Environmental and health impacts of air pollution: A review. Front. Public Health 8, 14 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Kelly F. J., Fussell J. C., Air pollution and public health: emerging hazards and improved understanding of risk. Environ. Geochem. Health 37, 631–649 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Zheng T., Zhang J., Sommer K., Bassig B. A., Zhang X., Braun J., Xu S., Boyle P., Zhang B., Shi K., Buka S., Liu S., Li Y., Qian Z., Dai M., Romano M., Zou A., Kelsey K., Effects of environmental exposures on fetal and childhood growth trajectories. Ann. Glob. Health 82, 41–99 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Mattison D. R., Environmental exposures and development. Curr. Opin. Pediatr. 22, 208–218 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Issah I., Duah M. S., Arko-Mensah J., Bawua S. A., Agyekum T. P., Fobil J. N., Exposure to metal mixtures and adverse pregnancy and birth outcomes: A systematic review. Sci. Total. Environ. 908, 168380 (2024). [DOI] [PubMed] [Google Scholar]

[R22] 22.Govarts E., Nieuwenhuijsen M., Schoeters G., Ballester F., Bloemen K., de Boer M., Chevrier C., Eggesbø M., Guxens M., Krämer U., Legler J., Martínez D., Palkovicova L., Patelarou E., Ranft U., Rautio A., Petersen M. S., Slama R., Stigum H., Toft G., Trnovec T., Vandentorren S., Weihe P., Kuperus N. W., Wilhelm M., Wittsiepe J., Bonde J. P., OBELIX/ENRIECO , Birth weight and prenatal exposure to polychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (DDE): A meta-analysis within 12 European birth cohorts. Environ Health Perspect. 120, 162–170 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Veber T., Dahal U., Lang K., Orru K., Orru H., Industrial air pollution leads to adverse birth outcomes: A systematized review of different exposure metrics and health effects in newborns. Public Health Rev. 43, 1604775 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Guo L. Q., Chen Y., Mi B. B., Dang S. N., Zhao D. D., Liu R., Wang H. L., Yan H., Ambient air pollution and adverse birth outcomes: A systematic review and meta-analysis. J. Zhejiang Univ. Sci. B 20, 238–252 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]

[R25] 25.Tran K. V., Casey J. A., Cushing L. J., Morello-Frosch R., Residential proximity to oil and gas development and birth outcomes in California: A retrospective cohort study of 2006–2015 births. Environ. Health Perspect. 128, 67001 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Casey J. A., Savitz D. A., Rasmussen S. G., Ogburn E. L., Pollak J., Mercer D. G., Schwartz B. S., Unconventional natural gas development and birth outcomes in Pennsylvania, USA. Epidemiology 27, 163–172 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Gaughan C., Sorrentino K. M., Liew Z., Johnson N. P., Clark C. J., Soriano M., Plano J., Plata D. L., Saiers J. E., Deziel N. C., Residential proximity to unconventional oil and gas development and birth defects in Ohio. Environ. Res. 229, 115937 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.McKenzie L. M., Guo R., Witter R. Z., Savitz D. A., Newman L. S., Adgate J. L., Birth outcomes and maternal residential proximity to natural gas development in rural Colorado. Environ. Health Perspect. 122, 412–417 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Proietti E., Röösli M., Frey U., Latzin P., Air pollution during pregnancy and neonatal outcome: A review. J. Aerosol Med. Pulm. Drug Deliv. 26, 9–23 (2013). [DOI] [PubMed] [Google Scholar]

[R30] 30.Power C., Kuh D., Morton S., From developmental origins of adult disease to life course research on adult disease and aging: Insights from birth cohort studies. Annu. Rev. Public Health 34, 7–28 (2013). [DOI] [PubMed] [Google Scholar]

[R31] 31.Crump C., An overview of adult health outcomes after preterm birth. Early Hum. Dev. 150, 105187 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Los Angeles County Department of Public Health, “Aliso Canyon Gas Leak Community Assessment for Public Health Emergency Response (CASPER)” (2016); http://publichealth.lacounty.gov/media/docs/CASPERFinalReport.pdf.

[R33] 33.Nianogo R. A., Benmarhnia T., O’Neill S., A comparison of quasi-experimental methods with data before and after an intervention: An introduction for epidemiologists and a simulation study. Int. J. Epidemiol. 52, 1522–1533 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Wing C., Simon K., Bello-Gomez R. A., Designing difference in difference studies: Best practices for public health policy research. Annu. Rev. Public Health 39, 453–469 (2018). [DOI] [PubMed] [Google Scholar]

[R35] 35.Bekkar B., Pacheco S., Basu R., Basu R., Denicola N., Association of air pollution and heat exposure with preterm birth, low birth weight, and stillbirth in the US: A systematic review. JAMA Netw. Open 3, e208243 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Gong X., Lin Y., Bell M. L., Zhan F. B., Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA. Environ. Int. 120, 181–198 (2018). [DOI] [PubMed] [Google Scholar]

[R37] 37.Hertz-Picciotto I., Commentary: When brilliant insights lead astray. Int. J. Epidemiol. 30, 1243–1244 (2001). [DOI] [PubMed] [Google Scholar]

[R38] 38.Blencowe H., Krasevec J., de Onis M., Black R. E., An X., Stevens G. A., Borghi E., Hayashi C., Estevez D., Cegolon L., Shiekh S., Ponce Hardy V., Lawn J. E., Cousens S., National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: A systematic analysis. Lancet Glob. Health 7, e849–e860 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Mayo Clinic Staff, Pregnancy week by week (Mayo Clinic, 2022); https://mayoclinic.org/healthy-lifestyle/pregnancy-week-by-week/in-depth/fetal-development/art-20045997.

[R40] 40.Sharma D., Shastri S., Sharma P., Intrauterine growth restriction: Antenatal and postnatal aspects. Clin. Med. Insights Pediatr. 10, 67–83 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Cutland C. L., Lackritz E. M., Mallett-Moore T., Bardají A., Chandrasekaran R., Lahariya C., Nisar M. I., Tapia M. D., Pathirana J., Kochhar S., Muñoz F. M., Low birth weight: Case definition & guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine 35, 6492–6500 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.H. Ritchie, M. Roser, Gender Ratio (Our World in Data, 2019); https://ourworldindata.org/gender-ratio.

[R43] 43.Fontanesi L., Verrocchio M. C., D’Ettorre M., Prete G., Ceravolo F., Marchetti D., The impact of catastrophic events on the sex ratio at birth: A systematic review. Am. J. Hum. Biol. 36, e24003 (2024). [DOI] [PubMed] [Google Scholar]

[R44] 44.Long Y., Chen Q., Larsson H., Rzhetsky A., Observable variations in human sex ratio at birth. PLOS Comput. Biol. 17, e1009586 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Grech V., Scherb H., Hurricane Katrina: Influence on the male-to-female birth ratio. Med. Princ. Pract. 24, 477–485 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Leung M., Kioumourtzoglou M. A., Raz R., Weisskopf M. G., Bias due to selection on live births in studies of environmental exposures during pregnancy: A simulation study. Environ. Health Perspect. 129, 47001 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Harville E. W., Pan K., Beitsch L., Sherchan S. P., Gonsoroski E., Uejio C., Lichtveld M. Y., Hurricane Michael and adverse birth outcomes in the Florida panhandle: Analysis of vital statistics data. Disaster Med. Public Health Prep. 17, e94 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Thompson D. R., Thorpe A. K., Frankenberg C., Green R. O., Duren R., Guanter L., Hollstein A., Middleton E., Ong L., Ungar S., Space-based remote imaging spectroscopy of the Aliso Canyon CH₄ superemitter. Geophys. Res. Lett. 43, 6571–6578 (2016). [Google Scholar]

[R49] 49.United State Census Bureau, American Community Survey 5-Year Data (2009-2022) (2023); https://census.gov/data/developers/data-sets/acs-5year.2020.html#list-tab-1806015614.

[R50] 50.OEHHA, CalEnviroScreen 4.0 (2023); https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40.

PERMALINK

Investigating the Aliso Canyon gas blowout disaster and adverse birth outcomes: A quasiexperimental approach

Kimberly C Paul

John Molitor

Yu Yu

Christina Batteate

Massimo Stafoggia

Sanjali Mitra

Qi Meng

Diane Garcia-Gonzales

Myles Cockburn

David Eisenman

Sudipto Banerjee

Honghu Liu

Michael Jerrett

Beate Ritz

Roles

Abstract

INTRODUCTION

RESULTS

Birth cohort and impact zone

Fig. 1. Impact zone map.

Descriptive statistics

Table 1. Demographics of the birth cohort, including births from 23 October 2010 to 24 October 2019.

Estimated effects of the blowout on LBW outcomes

Fig. 2. LBW results.

Table 2. Dose-response of distance of maternal address from the well and LBW.

Fig. 3. TLBW and preterm birth results.

Estimated effects of the blowout on preterm birth

Estimated effects of the blowout on infant sex prevalence

Sensitivity analyses

DISCUSSION

MATERIALS AND METHODS

Birth data and outcomes

Exposure assessment

Statistical analysis

Acknowledgments

Supplementary Materials

The PDF file includes:

Other Supplementary Material for this manuscript includes the following:

REFERENCES AND NOTES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases