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Peterson CJ, Rao MB, Palipana A, Manning ER, Vancil A, Ryan P, Brokamp C, Kramer E, Szczesniak RD, Gecili E. Robust identification of environmental exposures and community characteristics predictive of rapid lung disease progression. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175348. [PMID: 39117222 DOI: 10.1016/j.scitotenv.2024.175348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/24/2024] [Accepted: 07/28/2024] [Indexed: 08/10/2024]
Abstract
Environmental exposures and community characteristics have been linked to accelerated lung function decline in people with cystic fibrosis (CF), but geomarkers, the measurements of these exposures, have not been comprehensively evaluated in a single study. To determine which geomarkers have the greatest predictive potential for lung function decline and pulmonary exacerbation (PEx), a retrospective longitudinal cohort study was performed using novel Bayesian joint covariate selection methods, which were compared with respect to PEx predictive accuracy. Non-stationary Gaussian linear mixed effects models were fitted to data from 151 CF patients aged 6-20 receiving care at a CF Center in the midwestern US (2007-2017). The outcome was forced expiratory volume in 1 s of percent predicted (FEV1pp). Target functions were used to predict PEx from established criteria. Covariates included 11 routinely collected clinical/demographic characteristics and 45 geomarkers comprising 8 categories. Unique covariate selections via four Bayesian penalized regression models (elastic-net, adaptive lasso, ridge, and lasso) were evaluated at both 95 % and 90 % credible intervals (CIs). Resultant models included one to 6 geomarkers (air temperature, percentage of tertiary roads outside urban areas, percentage of impervious nonroad outside urban areas, fine atmospheric particulate matter, fraction achieving high school graduation, and motor vehicle theft) representing weather, impervious descriptor, air pollution, socioeconomic status, and crime categories. Adaptive lasso had the lowest information criteria. For PEx predictive accuracy, covariate selection from the 95 % CI elastic-net had the highest area under the receiver-operating characteristic curve (mean ± standard deviation; 0.780 ± 0.026) along with the 95 % CI ridge and lasso methods (0.780 ± 0.027). The 95 % CI elastic-net had the highest sensitivity (0.773 ± 0.083) while the 95 % CI adaptive lasso had the highest specificity (0.691 ± 0.087), suggesting the need for different geomarker sets depending on monitoring goals. Surveillance of certain geomarkers embedded in prediction algorithms can be used in real-time warning systems for PEx onset.
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Affiliation(s)
- Clayton J Peterson
- Division of Biostatistics and Bioinformatics, Environmental & Public Health Sciences, University of Cincinnati College of Medicine, CARE/Crawley Building, Suite E-870 3230, Eden Ave, Cincinnati, OH 45267, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA
| | - Marepalli B Rao
- Division of Biostatistics and Bioinformatics, Environmental & Public Health Sciences, University of Cincinnati College of Medicine, CARE/Crawley Building, Suite E-870 3230, Eden Ave, Cincinnati, OH 45267, USA
| | - Anushka Palipana
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA; School of Nursing, Duke University, 307 Trent Dr., Durham, NC 27710, USA
| | - Erika Rasnick Manning
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA
| | - Andrew Vancil
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA
| | - Patrick Ryan
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, 2600 Clifton Ave, Cincinnati, OH 45221, USA
| | - Cole Brokamp
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, 2600 Clifton Ave, Cincinnati, OH 45221, USA
| | - Elizabeth Kramer
- Department of Pediatrics, University of Cincinnati College of Medicine, 2600 Clifton Ave, Cincinnati, OH 45221, USA; Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA
| | - Rhonda D Szczesniak
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, 2600 Clifton Ave, Cincinnati, OH 45221, USA; Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA
| | - Emrah Gecili
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, MLC 5041, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, 2600 Clifton Ave, Cincinnati, OH 45221, USA.
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Gecili E, Brokamp C, Rasnick E, Afonso PM, Andrinopoulou ER, Dexheimer JW, Clancy JP, Keogh RH, Ni Y, Palipana A, Pestian T, Vancil A, Zhou GC, Su W, Siracusa C, Ryan P, Szczesniak RD. Built environment factors predictive of early rapid lung function decline in cystic fibrosis. Pediatr Pulmonol 2023; 58:1501-1513. [PMID: 36775890 PMCID: PMC10121820 DOI: 10.1002/ppul.26352] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/13/2023] [Accepted: 02/05/2023] [Indexed: 02/14/2023]
Abstract
BACKGROUND The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. OBJECTIVE To identify built environment characteristics predictive of rapid CF lung function decline. METHODS We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6-20 years, 2012-2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1 ) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. MEASUREMENTS AND MAIN RESULTS The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [-0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. CONCLUSION Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions.
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Affiliation(s)
- Emrah Gecili
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Cole Brokamp
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Erika Rasnick
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Pedro M. Afonso
- Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eleni-Rosalina Andrinopoulou
- Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Judith W. Dexheimer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - John P. Clancy
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Cystic Fibrosis Foundation, 4550 Montgomery Ave, Bethesda, MD, USA
| | - Ruth H. Keogh
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Yizhao Ni
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Anushka Palipana
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Teresa Pestian
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Andrew Vancil
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Grace Chen Zhou
- Division of Statistics and Data Science, Department of Mathematics, University of Cincinnati, 155B McMicken Hall, Cincinnati, OH, USA
- St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Weiji Su
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Christopher Siracusa
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Patrick Ryan
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
| | - Rhonda D. Szczesniak
- Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, 3333 Burnet Ave, Cincinnati, OH, USA
- Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH, USA
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