Abstract
Children exposed to disproportionately higher levels of air pollution experience worse health outcomes. In this population-based, observational registry study, we examine the association between air pollution and graft failure/death in children following liver transplantation (LT) in the U.S. We modeled the associations between air pollution (PM2.5) levels localized to the patient’s ZIP code at the time of transplant and graft failure or death using Cox proportional-hazards models in pediatric LT recipients aged <19 years in the U.S. from 2005-2015. In univariable analysis, high neighborhood PM2.5 was associated with a 56% increased hazard of graft failure/death (HR: 1.56; 95%CI: 1.32, 1.83; p<0.001). In multivariable analysis, high neighborhood PM2.5 was associated with a 54% increased risk of graft failure/death (HR: 1.54; 95%CI: 1.29, 1.83; p<0.001) after adjusting for race as a proxy for racism, insurance status, rurality, and neighborhood socioeconomic deprivation. Children living in high air pollution neighborhoods have an increased risk of graft failure and death post-transplant, even after controlling for sociodemographic variables. Our findings add further evidence that air pollution contributes to adverse health outcomes for children post-transplant and lay the groundwork for future studies to evaluate underlying mechanisms linking PM2.5 to adverse LT outcomes.
1. INTRODUCTION
Racial, ethnic, and socioeconomic health inequities in the U.S. extend across age groups and health conditions. Neighborhood-level measures of socioeconomic deprivation and racial segregation have been associated with poor health outcomes across diseases [2]. Moreover, there is an established link between social adversity and adverse outcomes in children undergoing liver transplantation (LT); children from socioeconomically deprived neighborhoods and racially minoritized children experience a greater burden of morbidity and mortality after transplant [3-7].
One aspect of the built environment that is inequitably distributed is air pollution, with a larger proportion of socioeconomically deprived and racially minoritized populations living in neighborhoods with increased air pollution [1,8-10]. This inequitable distribution of air pollution reflects historical policies, e.g., redlining, that have persistent effects on modern society, underscoring ongoing structural racism in the U.S. [1]. In global estimates, 6.7 million deaths worldwide each year are associated with exposure to air pollution [11]. While we know air pollution is a human health threat, little is known about the effects of air pollution on chronic disease outcomes in children [12]. Indeed, children are inherently exposed to higher levels of ambient air pollution relative to adults because of their higher respiratory rate and greater intake of air relative to their body weight compared to adults [8,9,13].
Fine particulate matter of size 2.5 micrometers or smaller, or PM2.5, a specific air pollutant, is a well-established risk factor for adverse long-term health outcomes [9,11,13-19]. PM2.5 tends to be more concentrated in neighborhoods with an increased density of traffic and power-generating factories [12,18]. Among adult kidney transplant recipients, PM2.5 was associated with higher odds of delayed graft function, one-year acute rejection, and elevated risk of mortality [15]. However, no studies to date have examined the association between ambient air pollution and outcomes in children following LT. Thus, in this study, we aimed to examine the association between PM2.5 and adverse outcomes in children after LT using data from the Scientific Registry of Transplant Recipients and the Center for Air, Climate, and Energy Solutions (CACES). Our hypothesis was that children residing in neighborhoods with higher PM2.5 levels will have an increased risk of graft failure/death.
2. METHODS
2.1. Study population
This study used data from the Scientific Registry of Transplant Recipients (SRTR). The SRTR data system includes data on all donors, wait-listed candidates, and transplant recipients in the U.S., submitted by the members of the Organ Procurement and Transplantation Network. The HRSA, US Department of Health and Human Services provides oversight to the activities of the Organ Procurement and Transplantation Network and SRTR contractors.
We identified pediatric LT recipients <19 years who received a first transplant between January 1, 2005 and December 31, 2015 in the U.S (n = 4284). Among these 4,284 pediatric LT recipients, 127 (0.3%) recipients were excluded due to missing ZIP code data and/or living in ZIP code areas without available annual PM2.5 data. Supplemental Table 1 compares demographic characteristics between the included and excluded patients.
2.2. Primary exposure
The primary exposure was annual PM2.5 concentration localized to the recipients’ ZIP code at the time of transplant as listed in SRTR. Annual ambient PM2.5 concentration data between 2005 and 2015 by census tract were obtained from Land Use Regression models developed by the Center for Air, Climate and Energy Solutions (CACES). CACES develops models of annual PM2.5 concentration using measurements from U.S. Environmental Protection Agency (EPA) regulatory monitors and information about land use and satellite-derived estimates of air pollution to predict PM2.5 concentrations without direct measurements. By overlapping census tract centroids with ZIP code tabulation areas, we matched census tracts to 2005-2009 ZIP codes using 2000 census boundaries and 2010-2015 ZIP codes to 2010 census boundaries in accordance with U.S. census tract boundaries that are redrawn every 10 years. We did so given that data in the SRTR only allow for matching at the ZIP code level. We subsequently linked the averaged PM2.5 data from all overlapping census tracts to pediatric LT recipient ZIP codes. We used concentration estimates developed by the Center for Air, Climate and Energy Solutions (CACES) using v1 empirical models as described in Kim et al., 2018 [20].
We dichotomized recipients’ PM2.5 exposure level above and below 12 μg/m3 in accordance with the U.S. Environmental Protection Agency (EPA) National Ambient Air Quality Standards for annual PM2.5. Levels >12 μg/m3 are considered harmful to public health [15,21].
Primary outcome
Our primary outcome was a composite outcome of graft failure/death censored at ten years post-transplant. We followed participants from the time of transplant until 10 years post-transplant, censoring for graft failure/death. For patients without documented graft failure/death, graft survival was censored at the date of last follow-up. We applied administrative censoring at 10 years post-transplantation for patients followed longer than 10 years.
Covariables
To identify the set of covariables necessary to quantify the direct effect of PM2.5 on our graft failure/death outcome, we employed a causal inference approach using a directed acyclic graph (Figure 1). We conceptualized race as a social construct, rather than a biological construct, and as a proxy for the deleterious health effects of racism; therefore, we included race in our multivariable models as a proxy for these social effects [22]. We classified racial groups according to available race data in SRTR. To evaluate whether air pollution impacts diagnosis, e.g., autoimmune hepatitis, we included recipient primary diagnosis at transplantation.
Figure 1.
Directed acyclic graph of the hypothesized causal pathway.
The solid boxes indicate measurable variables, while the dashed boxes indicate unmeasurable variables within the SRTR database. This diagram is the theoretical model of the hypothesized causal pathway for the impact of PM2.5 exposure on outcomes for children following liver transplantation.
To adjust for area-level socioeconomic conditions that might confound the relationship between PM2.5 exposure and graft failure, we used a validated index of neighborhood material deprivation based on six variables of the U.S. Census 2015 American Community Survey, contemporaneous with the study period, that has been used in other studies and modeled this as a continuous variable [4,5,22,23]. We included insurance status in our models as a surrogate for household socioeconomic status. We used the Rural-Urban Commuting Area (RUCA) codes from the U.S. Department of Agriculture to classify patients as rural or urban to further isolate the effects of PM2.5 exposure, matching the appropriate RUCA codes to the appropriate ZIP codes to ensure RUCA codes were contemporaneous for the study period [24]. For patients who received a transplant from 2005-2009, we used RUCA codes valid for 2000-2009; for patients who received a transplant from 2010-2015, we used RUCA codes valid for 2010-2019.
Statistical analyses
Patient characteristics were compared between those residing in ZIP codes with high PM2.5 levels and those residing in ZIP codes with low PM2.5 levels using the Wilcoxon rank-sum test for continuous variables and the Chi-square test for categorical variables. We followed patients from the time of transplant until 10 years post-transplant. We measured associations between PM2.5 and graft failure/death using Cox proportional-hazards models and visualized using Kaplan-Meier survival curves.
Statistical analyses were conducted using R. A p value of less than 0.05 was considered statistically significant. The study was deemed exempt from review by the University of California, San Francisco Institutional Review Board.
3. RESULTS
Study population
Table 1 depicts the baseline characteristics of the study population. Of the 4,157 patients included, 651 (16%) of our cohort resided in neighborhoods with high PM2.5 levels. Children living in high PM2.5 level neighborhoods were more likely to be of Black race, live in an urban and more socioeconomically deprived area, have a diagnosis of autoimmune hepatitis, have a higher median laboratory MELD/PELD score at transplant, and have a longer median cold ischemia time.
Table 1.
Baseline characteristics by PM2.5 exposure.
| Characteristics | Overall | High PM2.5 | Low PM2.5 | P value |
|---|---|---|---|---|
| Number of patients, n (%) | 4157 | 651 (16) | 3506 (84) | |
| Age at transplant, y, median (IQR) | 2.5 (0.9-10.1) | 2 (0.92-10.0) | 2.67 (0.92-10.1) | 0.09 |
| Sex, n (%) | ||||
| Female | 2100 (51) | 319 (49) | 1781 (51) | 0.42 |
| Ethnicity, n (%) | ||||
| Hispanic | 747 (18) | 112 (17) | 635 (18) | 0.62 |
| Race, n (%) | ||||
| White | 3086 (74) | 420 (65) | 2666 (76) | < 0.001 |
| Black | 780 (19) | 183 (28) | 597 (17) | |
| Other | 291 (7) | 48 (7) | 243 (7) | |
| Asian | 189 (5) | 40 (6) | 149 (4) | |
| Pacific Islander | 11 (0.3) | 0 (0) | 11 (0.3) | |
| Multiracial | 62 (2) | 8 (1) | 54 (2) | |
| Native American | 29 (0.7) | 0 (0) | 29 (0.8) | |
| Insurance | ||||
| Private | 1889 (45) | 294 (45) | 1595 (45) | 0.14 |
| Public | 2177 (52) | 336 (52) | 1841 (53) | |
| Other | 91 (2) | 21 (3) | 70 (2) | |
| Neighborhood deprivation, median (IQR) | 0.37 (0.30-0.46) | 0.39 (0.31-0.49) | 0.37 (0.30-0.45) | < 0.001 |
| Rurality, n (%) | ||||
| Urban | 3163 (76) | 564 (87) | 2599 (74) | < 0.001 |
| Rural | 655 (16) | 56 (9) | 599 (17) | |
| Recipient diagnosis, n (%) | ||||
| Biliary atresia | 1274 (31) | 187 (29) | 1087 (31) | 0.02 |
| Other cholestatic | 817 (20) | 122 (19) | 695 (20) | |
| Acute liver failure | 407 (10) | 65 (10) | 342 (10) | |
| Metabolic | 413 (10) | 59 (9) | 354 (10) | |
| Tumor | 344 (8.3) | 42 (6) | 302 (9) | |
| Autoimmune hepatitis | 186 (4) | 35 (5) | 151 (4) | |
| Other | 709 (17) | 139 (21) | 570 (16) | |
| Laboratory MELD/PELD at transplant, median (IQR) | 15 (4-25) | 18 (7-26) | 14 (3-25) | < 0.001 |
| Allocation MELD/PELD at transplant, median (IQR) | 26 (18-33) | 25 (17-32) | 26 (18-34) | 0.16 |
| Status 1a/1b, n (%) | 1204 (29) | 156 (24) | 1048 (30) | < 0.01 |
| Donor age at transplant, y, median (IQR) | 10 (2-21) | 10 (2-21) | 10 (2-21) | 0.45 |
| Living donor transplant, n (%) | 418 (10) | 68 (10) | 350 (10) | 0.77 |
| Cold ischemia time, h, median (IQR) | 6.5 (5.0-8.2) | 6.8 (5.0-9.0) | 6.5 (5-8.1) | 0.02 |
Empty cells in the P value column are because P values represent comparisons across all categories of a variable. Low PM2.5 was classified as an annual mean PM2.5 exposure of < 12 μg/m3, and high PM2.5 was classified as an annual mean PM2.5 exposure of > 12 μg/m3 in accordance with EPA standards. IQR = interquartile range; MELD = Model for End-Stage Liver Disease; PELD = Pediatric End-Stage Liver Disease.
Supplemental Table 1 depicts demographic characteristics of excluded patients. Excluded patients were more likely to be non-Hispanic White, less likely to have private or public insurance and more likely to have “Other” insurance, more likely to have a diagnosis of other cholestatic disease, metabolic disease, or other disease, less likely to have a lower median laboratory MELD/PELD score at transplant, and less likely to be of status 1a/1b, the highest priority on the waitlist (Supplemental Table 1).
Graft Failure/Death Outcomes
The overall 1-, 5-, and 10-year graft survival in our cohort was 85%, 75%, and 71%, respectively. There was no difference in estimated graft survival rates at 1 year between patients living in ZIP codes with high PM2.5 and low PM2.5 (p = 0.09). Patients from high PM2.5 neighborhoods compared to those from low PM2.5 neighborhoods had lower estimated graft survival rates at 5 years (75% vs. 83%; p < 0.001) and 10 years (71% vs. 81%; p < 0.001). Figure 2 depicts a Kaplan-Meier survival curve for children living in high and low PM2.5 neighborhoods. In univariable analysis (Table 2), high neighborhood PM2.5 was associated with a 56% increased hazard of graft failure/death (hazard ratio [HR]: 1.56; 95% CI: 1.32-1.83; p < 0.001). Black children had a 24% increased hazard of graft failure/death (HR: 1.24; 95% CI: 1.05-1.46; p = 0.01) compared with White children, and for every 0.1 increase in deprivation index score, there was a 14% increased hazard of graft failure/death (HR: 1.14; 95% CI: 1.08-1.21; p < 0.001). Public insurance was associated with a 36% increased hazard of graft failure/death compared with private insurance (HR: 1.36; 95% CI: 1.19-1.56; p < 0.001). Rural residence status was associated with a 25% increased hazard of graft failure/death in univariable analysis (HR: 1.25; 95% CI: 1.05-1.48; p = 0.01).
Figure 2.
Kaplan-Meier curve of children after liver transplant by PM2.5 exposure.
Table 2.
Univariable Cox proportional-hazards models on composite outcome of graft failure/death, whichever occurred first, at 10 years post-transplant.
| Graft failure or death | |||
|---|---|---|---|
| Characteristics | Hazard Ratio | 95% CI | P value |
| High neighborhood PM2.5 | 1.56 | 1.32-1.83 | < 0.001 |
| Age at transplant | 1.00 | 1.00-1.00 | 0.79 |
| Gender | |||
| Female | 0.97 | 0.85-1.11 | 0.69 |
| Ethnicity | |||
| Hispanic | 1.03 | 0.86-1.22 | 0.77 |
| Race | |||
| White | Ref | Ref | |
| Black | 1.24 | 1.05-1.46 | 0.01 |
| Asian | 1.01 | 0.73-1.41 | 0.95 |
| Pacific Islander | 0.42 | 0.06-3.01 | 0.39 |
| Multiracial | 1.07 | 0.62-1.85 | 0.82 |
| Native American | 1.64 | 0.85-3.17 | 0.14 |
| Primary insurance | |||
| Private | Ref | Ref | |
| Public | 1.36 | 1.19-1.56 | < 0.001 |
| Other | 0.98 | 0.60-1.62 | 0.95 |
| Neighborhood deprivation | 1.14 | 1.08-1.21 | < 0.001 |
| Rurality | |||
| Urban | Ref | Ref | |
| Rural | 1.25 | 1.05-1.48 | 0.01 |
| Recipient diagnosis | |||
| Biliary atresia | Ref | Ref | |
| Other cholestatic | 1.67 | 1.36-2.04 | < 0.001 |
| Acute liver failure | 1.67 | 1.31-2.13 | < 0.001 |
| Metabolic | 1.22 | 0.93-1.59 | 0.16 |
| Tumor | 1.72 | 1.33-2.22 | < 0.001 |
| Autoimmune hepatitis | 2.05 | 1.52-2.77 | < 0.001 |
| Other | 1.68 | 1.37-2.07 | < 0.001 |
| Laboratory MELD/PELD at transplant, median (IQR) | 1.01 | 1.01-1.02 | < 0.001 |
| Allocation MELD/PELD at transplant, median (IQR) | 1.00 | 0.99-1.00 | 0.5 |
| Status 1a/1b, n (%) | 1.27 | 1.10-1.46 | 0.001 |
| Donor age at transplant, y, median (IQR) | 1.00 | 1.00-1.01 | 0.71 |
| Living donor transplant, n (%) | 0.62 | 0.47-0.81 | < 0.001 |
| Cold ischemia time, h, median (IQR) | 1.02 | 1.00-1.04 | 0.04 |
To measure the total and direct effects of PM2.5 on graft failure/death, we built a multivariable model adjusting for neighborhood socioeconomic deprivation and rurality. In this analysis, high neighborhood PM2.5 was associated with a 53% increased hazard of graft failure/death (HR: 1.53; 95% CI: 1.29-1.83; p < 0.001) (Table 3). In multivariable analysis adjusting for all available sociodemographic variables (race, insurance status, neighborhood socioeconomic deprivation, and rurality), high neighborhood PM2.5 was associated with a 54% increased hazard of graft failure (HR: 1.54; 95% CI: 1.29-1.83; p < 0.001). In multivariable analysis adjusting for all available sociodemographic variables and recipient primary diagnosis, high neighborhood PM2.5 was associated with a 52% increased hazard of graft failure (HR: 1.52; 95% CI: 1.28-1.82; p < 0.001). To assess whether air pollution leads to worse baseline liver dysfunction and subsequently worse outcomes, we conducted a sensitivity analysis adjusting for diagnosis and MELD/PELD at transplant. After adjusting for all available sociodemographic variables, recipient primary diagnosis, and MELD/PELD, high neighborhood PM2.5 was associated with a 50% increased hazard of graft failure (HR: 1.50; 95% CI: 1.25-1.79; p < 0.001) (Supplemental Table 2).
Table 3.
Multivariable Cox proportional-hazards models on composite outcome of graft failure/death, whichever occurred first, at 10 years post-transplant.
|
Model 1: Adjusting for minimum covariates necessary to measure the total effect of PM2.5 on graft failure/death |
Model 2: Adjusting for all available sociodemographic variables (PM2.5 + race + insurance + deprivation + rurality) |
Model 3: Adjusting for all available sociodemographic variables + primary diagnosis (PM2.5 + race + insurance + primary diagnosis + deprivation + rurality) |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Variables | HR | 95% CI | P value | HR | 95% CI | P value | HR | 95% CI | P value |
| PM2.5 | 1.53 | 1.29-1.83 | < 0.001 | 1.54 | 1.29-1.83 | < 0.001 | 1.52 | 1.28-1.82 | < 0.001 |
| Race | |||||||||
| White | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref |
| Black | 1.12 | 0.94-1.35 | 0.21 | 1.15 | 0.96-1.39 | 0.12 | |||
| Asian | 1.02 | 0.70-1.52 | 0.87 | 1.08 | 0.73-1.58 | 0.70 | |||
| Pacific Islander | 0.55 | 0.08-3.92 | 0.55 | 0.59 | 0.08-4.19 | 0.60 | |||
| Multiracial | 1.14 | 0.62-2.07 | 0.67 | 1.15 | 0.63-2.10 | 0.65 | |||
| Native American | 1.55 | 0.79-3.04 | 0.20 | 1.55 | 0.79-3.04 | 0.20 | |||
| Insurance | |||||||||
| Private | Ref | Ref | Ref | Ref | Ref | Ref | |||
| Public | 1.19 | 1.01-1.40 | 0.04 | 1.21 | 1.02-1.43 | 0.03 | |||
| Other | 0.96 | 0.57-1.61 | 0.77 | 0.93 | 0.55-1.57 | 0.80 | |||
| Deprivation | 1.13 | 1.06-1.21 | < 0.001 | 1.09 | 1.01-1.17 | 0.02 | 1.09 | 1.02-1.17 | 0.02 |
| Rurality | |||||||||
| Urban | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref |
| Rural | 1.29 | 1.07-1.55 | < 0.01 | 1.28 | 1.06-1.55 | 0.01 | 1.25 | 1.03-1.51 | 0.02 |
| Recipient diagnosis | |||||||||
| Biliary atresia | Ref | Ref | Ref | ||||||
| Other cholestatic | 1.84 | 1.47-2.30 | < 0.001 | ||||||
| Acute liver failure | 1.61 | 1.22-2.13 | < 0.001 | ||||||
| Metabolic | 1.53 | 1.14-2.06 | < 0.01 | ||||||
| Tumor | 1.78 | 1.33-2.39 | < 0.001 | ||||||
| Autoimmune hepatitis | 2.26 | 1.60-3.19 | < 0.001 | ||||||
| Other | 1.85 | 1.47-2.33 | < 0.001 | ||||||
4. DISCUSSION
This is the first study examining the impact of air pollution on pediatric liver transplant outcomes. We found that children living in neighborhoods with high air pollution were at increased risk of graft failure/death. This finding persisted even after adjusting for several sociodemographic variables, recipient primary diagnosis, and MELD/PELD, suggesting that air pollution is independently associated with graft failure/death following LT in children. Indeed, approximately 3 in 10 children living in ZIP codes with high air pollution will experience graft failure/death within 10 years of transplantation.
Our study adds to the expanding body of literature exploring the toxic effects of air pollution on transplant outcomes. Data from the EPA reveal that air pollution is projected to increase, underscoring the need to understand its implications on a range of chronic disease outcomes [13,18,25], especially for those belonging to Black and other minoritized communities [1,2,8-10,16,18,26]. In our sample, more than 90% of children living in ZIP codes with high PM2.5 were also living in urban areas, which is not surprising considering the increased density of highways, automotive exhaust, and factories in urban areas. However, the effect of high PM2.5 persisted, even when we adjusted for urban/rural residence status, strengthening the notion that air pollution is a direct contributor to adverse pediatric LT outcomes and that deleterious effects of high PM2.5 are prevalent among both urban and rural environments. Additionally, this finding held in our three sensitivity analyses in adjusting for all available sociodemographic variables, as well as for primary recipient diagnosis and MELD/PELD, further evidence that air pollution is a direct contributor to adverse pediatric LT outcomes. These persistent findings even after controlling for multiple sociomedical variables suggest that structural solutions to reduce air pollution and mitigate exposure may improve pediatric LT outcomes.
It remains unknown why air pollution might lead to adverse outcomes in solid organ transplantation. One potential mechanism for the effect of high PM2.5 on graft failure is the systemic inflammation induced by fine particulate matter. Previous studies have demonstrated mechanistic pathways in which air pollution causes toxic effects, including systemic inflammation mediated by oxidative stress from inflammatory cells that can generate reactive oxygen species, immune modulation, and epigenetic effects, which are known to cause various adverse health outcomes [17,27,28]. This study lays the groundwork for future mechanistic studies that uncover the link between PM2.5 and graft failure/death in pediatric LT [29,30].
Nonetheless, air pollution mitigation strategies at both individual and structural levels could be important for improving outcomes for children after liver transplant. Such mitigation strategies at the individual level include the use of air purifiers indoors, respirator use when outdoors, and smoking cessation [31]. At the structural level, policies limiting the proximity of childcare facilities and schools to traffic could especially reduce exposure for children, as many children in the U.S. attend schools in high-traffic areas [32]. This study lays the groundwork for future studies to determine whether mitigating air pollution improves pediatric liver transplant outcomes.
We acknowledge the following limitations of this study. Extrapolating group-level exposures to patient-level outcomes may risk committing ecological fallacy [33,34]. Indeed, actual exposure to PM2.5 can vary widely at the individual recipient level due to the differential proximity to sources of air pollution, including indoor air pollution, which we were unable to control for in our analyses. However, CACES provides a well-established model of air pollution using data from the U.S. EPA. Secondly, for our study cohort, we used air pollution levels from 2005 to 2015 because CACES currently provides granular air pollution data only up until the year 2015. However, per the U.S. EPA, the mean national PM2.5 concentrations from 2015-2022 have only changed marginally [21]. Third, we matched PM2.5 levels by census tract to liver transplant recipients at the ZIP code level because SRTR only collects patient ZIP codes. ZIP codes are designated by the U.S. Postal Service (USPS) to optimize efficiency in mail delivery; therefore, they can be adjusted to increase such efficiency. ZIP codes can also vary greatly in size and do not always include a contextually homogenous group of residents due to their design for the USPS and not to describe populations. However, ZIP codes are a small geographic unit that is readily available in the SRTR registry. Fourth, we used patient ZIP codes from their residence at the time of liver transplant, the only available ZIP code data from the SRTR. This does not account for families who may have relocated residences, the impact of air pollution within a ZIP code in which the transplant recipient lived for the majority of time post-transplant until graft failure/death, or for secular variations in annual mean PM2.5 exposure at the ZIP code level. Given air pollution varies in both directions, i.e., exposures could be higher or lower than captured, we would expect a non-differential misclassification error between the low PM2.5 and high PM2.5 groups, which would bias our findings toward the null. That we still see an effect of PM2.5 suggests a true association between PM2.5 and graft failure/death. Moreover, although many families relocate residences, there are data on residential mobility and pollutant exposure demonstrating that people move to neighborhoods with similar levels of pollution [35,36]. Fifth, we acknowledge the potential for unmeasured confounders that predispose patients to both higher levels of air pollution and adverse post-transplant outcomes that may account for our findings, including extremes of weather due to climate change. However, the effect of high PM2.5 persisted even after including several socioeconomic indicators and urban/rural residence status, suggesting that air pollution may have a direct impact on adverse post-transplant outcomes. Characterizing the impact of extreme weather events on air pollution variability was outside the scope of available CACES data. Finally, there were limitations common to registry studies, such as missing data. However, the SRTR database is the most robust and comprehensive data source that exists for transplant recipients and remains one of the most objective and well-maintained datasets for studying children undergoing transplantation.
This study is the first to demonstrate a link between air pollution and adverse outcomes in children who have undergone liver transplantation. Our findings reveal that elevated neighborhood air pollution levels are associated with an increased risk of graft failure/death in this patient population. Considering the unequal distribution of air pollution exposure, investigating the mechanisms linking air pollution and negative outcomes following liver transplantation is particularly crucial and may further elucidate how social adversity impacts vulnerable children after transplantation.
Supplementary Material
Acknowledgments:
The data reported here have been supplied by the Hennepin Healthcare Research Institute (HHRI) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government.
Funding/Support:
This study was supported by the following grants: UCSF Liver Center P30 DK026743 (SIW, JCL) and NIDDK K23 K23DK132454 (SIW). This study was also supported by the UCSF School of Medicine Inquiry Office and the Tamir Miloh Award, Society of Pediatric Liver Transplantation (JEY).
Abbreviations:
- CACES
Center for Air, Climate, and Energy Solutions
- EPA
Environmental Protection Agency
- LT
liver transplantation
- MELD
Model for End-Stage Liver Disease
- PELD
Pediatric End-Stage Liver Disease
- PM2.5
fine particulate matter with diameters 2.5 micrometers and smaller
- SRTR
Scientific Registry of Transplant Recipients
Footnotes
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Disclosures: The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability statement:
Data are not shared.
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