Association Between Particulate Matter Pollution and CKD Mortality by Social Deprivation

Abstract

Chronic kidney disease (CKD) remains an important driver of mortality in the United States.1Chronic Kidney Disease in the United States, 2021.https://www.cdc.gov/kidneydisease/publications-resources/ckd-national-facts.htmlDate accessed: May 31, 2022Google Scholar Traditional risk factors such as diabetes and hypertension only partially explain geographical variation in CKD mortality. There is increasing evidence that environmental and socioeconomic factors are key determinants of health outcomes in CKD. Recent findings suggest that air pollution, specifically particulate matter <2.5 microns (PM2.5), may be an important risk factor for CKD-related morbidity and mortality.2Jung J. Park J.Y. Kim Y.C. et al.Effects of air pollution on mortality of patients with chronic kidney disease: a large observational cohort study.Sci Total Environ. 2021; 786147471https://doi.org/10.1016/j.scitotenv.2021.147471Crossref PubMed Scopus (12) Google Scholar Further, social deprivation may increase susceptibility to PM2.5-mediated health effects,3Bevan G.H. Freedman D.A. Lee E.K. Rajagopalan S. Al-Kindi S.G. Association between ambient air pollution and county-level cardiovascular mortality in the United States by social deprivation index.Am Heart J. 2021; 235: 125-131https://doi.org/10.1016/j.ahj.2021.02.005Crossref PubMed Scopus (10) Google Scholar but it is unknown if this extrapolates to CKD. The objective of this cross-sectional study was to quantify the association between PM2.5 and CKD mortality by levels of social deprivation at the US county level. We linked 1999-2019 county-level age-adjusted CKD mortality data from the National Center for Health Statistics Multiple Cause of Death files (CKD mortality defined as underlying cause of death given as ICD10 code N18.X) with the Social Deprivation Index (SDI) and chronic (1999-2019) PM2.5 exposures. ICD-10 codes have shown good accuracy in identifying patients with moderate-to-severe CKD.4Paik J.M. Patorno E. Zhuo M. et al.Accuracy of identifying diagnosis of moderate to severe chronic kidney disease in administrative claims data.Pharmacoepidemiol Drug Saf. 2022; 31: 467-475https://doi.org/10.1002/pds.5398Crossref PubMed Scopus (9) Google Scholar The SDI, a validated metric of socioeconomic status, grades counties from 1 to 100 by increasing social deprivation.5Butler D.C. Petterson S. Phillips R.L. Bazemore A.W. Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery.Health Serv Res. 2013; 48: 539-559https://doi.org/10.1111/j.1475-6773.2012.01449.xCrossref PubMed Scopus (215) Google Scholar We used the 2015 SDI, which was generated by incorporating 2011-2015 American Community Survey estimates (for low income, low education, non-employment, no car ownership, crowded housing, renter-occupied housing, and single-parent family home) weighted by factor loadings. County-level PM2.5 estimates were generated by QGIS V3.22 using modeled data by the Atmospheric Analysis Group (Item S1).6Hammer M.S. van Donkelaar A. Li C. et al.Global estimates and long-term trends of fine particulate matter concentrations (1998–2018).Environ Sci Technol. 2020; 54: 7879-7890https://doi.org/10.1021/acs.est.0c01764Crossref PubMed Scopus (258) Google Scholar We grouped counties by SDI quartile, then used linear regression models to estimate the associations between PM2.5 and age-adjusted CKD mortality standardized to the 2000 US Census population (accounting for population change with time). Further, local spatial auto-correlations (Moran’s I) were modeled to identify statistically significant clusters of counties with high and low CKD mortality rates and PM2.5 exposures. Research ethics approval and informed consent were not required, since the data are publicly available and de-identified. We analyzed 469,933 deaths due to CKD across 2,304 counties with mean age-adjusted CKD mortality of 7.70 deaths per 100,000 person-years. There was significant regional variation in age-adjusted CKD mortality and in PM2.5 (Fig S1). Counties with high SDI had higher PM2.5 exposure, albeit with significant overlap in PM2.5 levels between groups (Fig S2). The univariate linear regression model showed ∼18% of intercountry variation in CKD mortality was explained by PM2.5 variation alone, with a greater CKD mortality rate of 0.70 deaths per 100,000 person-years for every 1-μg/m3 greater PM2.5 exposure. The association was attenuated when adding SDI to the model, but both PM2.5 (β, 0.57 [95% CI, 0.52-0.62]; P < 0.001) and SDI (β, 0.05 [95% CI, 0.05-0.06]; P < 0.001) remained independently associated with age-adjusted CKD mortality (adjusted R2 = 0.37). Similar estimates were obtained using random effects stratified by US census region. The association between PM2.5 and CKD mortality was strongest among counties with highest SDI (β of 0.70 [95% CI, 0.49-0.92], vs 0.49 [0.41-0.56] for lowest SDI; P for interaction <0.001; Fig 1). Fig 2 shows CKD mortality across the spectrum of PM2.5 and SDI intersections. Positive and negative spatial autocorrelations were noted in PM2.5 and CKD mortality (Fig S3).Figure 2Association between PM2.5 and CKD mortality with SDI interaction; highest (red) and lowest (blue) CKD mortality rates are achieved when PM2.5 and SDI are simultaneously high or low, respectively.View Large Image Figure ViewerDownload Hi-res image Download (PPT) In this analysis of ∼0.5 million CKD deaths, we illustrate that PM2.5 is associated with age-adjusted CKD mortality even after adjusting for social deprivation. We further show that the PM2.5-CKD association is accentuated in counties that are most socially vulnerable, and that 37% of intercountry variation of CKD mortality can be explained by PM2.5 and SDI alone. This analysis highlights the important contributions of social and environmental drivers of CKD. Air pollutants can increase CKD risk via multiple mechanisms, including inflammation, oxidative stress, thrombosis, and elevations in blood pressure and glycemia.7Ye Z. Li X. Han Y. Wu Y. Fang Y. Association of long-term exposure to PM2.5 with hypertension and diabetes among the middle-aged and elderly people in Chinese mainland: a spatial study.BMC Public Health. 2022; 22: 569https://doi.org/10.1186/s12889-022-12984-6Crossref PubMed Scopus (6) Google Scholar, 8Kuźma Ł. Małyszko J. Bachórzewska-Gajewska H. Kralisz P. Dobrzycki S. Exposure to air pollution and renal function.Sci Rep. 2021; 1111419https://doi.org/10.1038/s41598-021-91000-0Crossref Scopus (17) Google Scholar, 9Eze I.C. Hemkens L.G. Bucher H.C. et al.Association between ambient air pollution and diabetes mellitus in Europe and North America: systematic review and meta-analysis.Environ Health Perspect. 2015; 123: 381-389https://doi.org/10.1289/ehp.1307823Crossref PubMed Scopus (351) Google Scholar As has been observed for cardiovascular disease,3Bevan G.H. Freedman D.A. Lee E.K. Rajagopalan S. Al-Kindi S.G. Association between ambient air pollution and county-level cardiovascular mortality in the United States by social deprivation index.Am Heart J. 2021; 235: 125-131https://doi.org/10.1016/j.ahj.2021.02.005Crossref PubMed Scopus (10) Google Scholar our findings suggest that people with CKD may be more vulnerable to the effects of air pollution if they live in socially deprived areas. The mechanisms driving disproportionately greater PM2.5-related CKD mortality among socially deprived individuals may include health care access, behavioral risk factors, work-related exposures, and prevalence of other comorbidities that might signal increased susceptibility to the effects of air pollution.10Makri A. Stilianakis N.I. Vulnerability to air pollution health effects.Int J Hyg Environ Health. 2008; 211: 326-336https://doi.org/10.1016/j.ijheh.2007.06.005Crossref PubMed Scopus (145) Google Scholar A main limitation of this study is that the accuracy of CKD mortality defined by ICD-10 codes has not been validated. Further, our analysis is limited by population-level data that may not account for all confounders, modeled environmental exposures, temporal heterogeneity of data, and possible misclassification of cause of death. We tried to minimize these limitations by using large-scale age-adjusted mortality and adjusting for SDI, a conglomerate of socioeconomic variables. Our findings call for increased recognition of geographical disparities in CKD mortality and their socioenvironmental drivers. Interventions curbing PM2.5 may be most impactful to reduce CKD mortality in socioeconomically deprived areas. Research idea and study design: IM, SA-K; data acquisition: IM, JS, MEEM; data analysis/interpretation: IM, JS, MEEM, SA-K; statistical analysis: IM, JS, SA-K; supervision or mentorship: MD, MR, SR, SA-K. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. This work was partly funded by National Institute on Minority Health and Health Disparities award #P50MD017351. The funder did not have a role in study design, data collection, analysis, reporting, or the decision to submit for publication. The authors declare that they have no relevant financial interests. Received June 14, 2022. Evaluated by 3 external peer reviewers, with direct editorial input from a Statistics/Methods Editor, an Associate Editor, and a Deputy Editor who served as Acting Editor-in-Chief. Accepted in revised form September 14, 2022. The involvement of an Acting Editor-in-Chief was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies. Download .pdf (.43 MB) Help with pdf files Supplementary File (PDF)Figures S1-S3; Item S1.