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Danelle T Lobdell  - - - 
Top co-authors See all
Jennifer Horney

28 shared publications

Epidemiology, University of Delaware College of Health Sciences, Newark, DE, USA

Elaine A. Cohen Hubal

27 shared publications

Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States

Laura Jackson

17 shared publications

Office of Research and Development, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA

Edward Hudgens

16 shared publications

United States Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, MD 58-C, 109. T.W. Alexander Drive, Research Triangle Park, NC 27711, USA

Kristen M. Rappazzo

16 shared publications

Office of Research and DevelopmentU.S. Environmental Research Triangle Park Research Triangle Park North Carolina

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Distribution of Articles published per year 
(2003 - 2018)
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Publications See all
Article 1 Read 0 Citations Associations between environmental quality and infant mortality in the United States, 2000–2005 Achal P. Patel, Jyotsna S. Jagai, Lynne C. Messer, Christine... Published: 15 October 2018
Archives of Public Health, doi: 10.1186/s13690-018-0306-0
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The United States (U.S.) suffers from high infant mortality (IM) rates and there are significant racial/ethnic differences in these rates. Prior studies on the environment and infant mortality are generally limited to singular exposures. We utilize the Environmental Quality Index (EQI), a measure of cumulative environmental exposure (across air, water, land, sociodemographic, and land domains) for U.S. counties from 2000 to 2005, to investigate associations between ambient environment and IM across maternal race/ethnicity. We linked 2000–2005 infant data from the U.S. Centers for Disease Control and Prevention to the EQI (n = 22,702,529; 144,741 deaths). We utilized multi-level regression to estimate associations between quartiles of county-level EQI and IM. We also considered associations between quartiles of county level domain specific indices with IM. We controlled for rural-urban status (RUCC1: urban, metropolitan; RUCC2: urban, non-metropolitan; RUCC3: less urbanized; RUCC4: thinly populated), maternal age, maternal education, marital status, infant sex, and stratified on race/ethnicity. Additionally, we estimated associations for linear combinations of environmental quality and rural-urban status. We found a mix of positive, negative, and null associations and our findings varied across domain and race/ethnicity. Poorer overall environmental quality was associated with decreased odds among Non-Hispanic whites (OR and 95% CI: EQIQ4 (ref. EQIQ1): 0.84[0.80,0.89]). For Non-Hispanic blacks and Hispanics, some increased odds were observed. Poorer air quality was monotonically associated with increased odds among Non-Hispanic whites (airQ4 (ref. airQ1): 1.05[0.99,1.11]) and blacks (airQ4 (ref. airQ1): 1.09 [0.9,1.31]). Rural status was associated with increased IM odds among Hispanics (RUCC4-Q4:1.36[1.04,1.78]; RUCC1-Q4: 1.04[0.92,1.16], ref. for both RUCC1-Q1). This study is the first to report on associations between ambient environmental quality and IM across the United States. It corroborates prior research suggesting an association between air pollution and IM and identifies residence in thinly populated (rural) areas as a potential risk factor towards IM amongst Hispanics. Some of the counterintuitive findings highlight the need for additional research into potentially differential drivers of environmental quality across the rural-urban continuum, especially with regards to the sociodemographic environment. The online version of this article (10.1186/s13690-018-0306-0) contains supplementary material, which is available to authorized users.
Article 0 Reads 0 Citations Associations between environmental quality and adult asthma prevalence in medical claims data Christine L. Gray, Danelle T. Lobdell, Kristen M. Rappazzo, ... Published: 01 October 2018
Environmental Research, doi: 10.1016/j.envres.2018.06.020
DOI See at publisher website
Article 0 Reads 1 Citation The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adu... Christine L. Gray, Lynne C. Messer, Kristen M. Rappazzo, Jyo... Published: 30 August 2018
PLOS ONE, doi: 10.1371/journal.pone.0203301
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Physical inactivity is a primary contributor to the obesity epidemic, but may be promoted or hindered by environmental factors. To examine how cumulative environmental quality may modify the inactivity-obesity relationship, we conducted a cross-sectional study by linking county-level Behavioral Risk Factor Surveillance System data with the Environmental Quality Index (EQI), a composite measure of five environmental domains (air, water, land, built, sociodemographic) across all U.S. counties. We estimated the county-level association (N = 3,137 counties) between 2009 age-adjusted leisure-time physical inactivity (LTPIA) and 2010 age-adjusted obesity from BRFSS across EQI tertiles using multi-level linear regression, with a random intercept for state, adjusted for percent minority and rural-urban status. We modelled overall and sex-specific estimates, reporting prevalence differences (PD) and 95% confidence intervals (CI). In the overall population, the PD increased from best (PD = 0.341 (95% CI: 0.287, 0.396)) to worst (PD = 0.645 (95% CI: 0.599, 0.690)) EQI tertile. We observed similar trends in males from best (PD = 0.244 (95% CI: 0.194, 0.294)) to worst (PD = 0.601 (95% CI: 0.556, 0.647)) quality environments, and in females from best (PD = 0.446 (95% CI: 0.385, 0.507)) to worst (PD = 0.655 (95% CI: 0.607, 0.703)). We found that poor environmental quality exacerbates the LTPIA-obesity relationship. Efforts to improve obesity through LTPIA may benefit from considering this relationship.
Article 0 Reads 5 Citations County-level cumulative environmental quality associated with cancer incidence Jyotsna S. Jagai, Lynne C. Messer, Kristen M. Rappazzo, Chri... Published: 08 May 2017
Cancer, doi: 10.1002/cncr.30709
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Individual environmental exposures are associated with cancer development; however, environmental exposures occur simultaneously. The Environmental Quality Index (EQI) is a county-level measure of cumulative environmental exposures, occurring in five domains. The EQI was linked to county-level annual age-adjusted cancer incidence rates from the Surveillance, Epidemiology, and End Results (SEER) Program State Cancer Profiles. All-site cancer and the top three site-specific cancers for males and females were considered. Incident rate differences (IRD, annual rate difference per 100,000 persons) and 95% confidence intervals (CI) were estimated using fixed-slope, random intercept multi-level linear regression models. Associations were assessed with domain-specific indices and analyses were stratified by rural-urban status. Comparing highest quintile/poorest environmental quality to lowest quintile/best environmental quality for overall EQI, all-site county-level cancer incidence rate was positively associated with poor environmental quality overall (IRD: 38.55, 95%CI 29.57,47.53) and for males (IRD: 32.60, 95%CI 16.28,48.91) and females (IRD: 30.34, 95%CI 20.47,40.21), indicating a potential increase in cancer incidence with decreasing environmental quality. Rural-urban stratified models demonstrated positive associations comparing highest to lowest quintiles for all strata, except the thinly populated/rural stratum and in the metropolitan-urbanized stratum. Prostate and breast cancer demonstrated the strongest positive associations with poor environmental quality. We observed strong positive associations between the EQI and all-site cancer incidence rates and associations differed by rural-urban status and environmental domain. Research focusing on single environmental exposures in cancer development may not address the broader environmental context in which cancers develop and future research needs to consider cumulative environmental exposures.
Article 0 Reads 1 Citation The comparison of gestational dating methods and implications for exposure-outcome associations: an example with PM2.5 a... Kristen M Rappazzo, Danelle T Lobdell, Lynne C Messer, Charl... Published: 25 October 2016
Occupational and Environmental Medicine, doi: 10.1136/oemed-2016-103833
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Objectives: Estimating gestational age is usually based on date of last menstrual period (LMP) or clinical estimation (CE); both approaches introduce potential bias. Differences in methods of estimation may lead to misclassification and inconsistences in risk estimates, particularly if exposure assignment is also gestation-dependent. This paper examines a “what-if” scenario in which alternative methods are used and attempts to elucidate how method choice affects observed results. Methods: We constructed two 20-week gestational age cohorts of pregnancies between 2000–2005 (NJ, PA, OH) using live birth certificates: one defined preterm birth (PTB) status using CE and one using LMP. Within these, we estimated risk for 4 categories of preterm birth (PTBs per 106 pregnancies) and risk differences (RD (95% confidence intervals)) associated with exposure to particulate matter (PM2.5). Results: More births were classified preterm using LMP (16%) compared with CE (8%). RD divergences increased between cohorts as exposure period approached delivery. Among births between 28–31 weeks, week 7 PM2.5 exposure conveyed RDs of 44(21, 67) for CE and 50(18, 82) for LMP populations; while week 24 exposure conveyed RDs of 33(11, 56) and −20(−50, 10), respectively. Conclusions: Different results from analyses restricted to births with both CE and LMP are likely due to differences in dating methods rather than selection issues. Results are sensitive to choice of gestational age estimation, though degree of sensitivity can vary by the exposure timing. When both outcome and exposure depend on estimate of gestational age, awareness of nuances in method used for estimation is critical.
Article 0 Reads 0 Citations Additive Interaction between Heterogeneous Environmental Quality Domains (Air, Water, Land, Sociodemographic, and Built ... Shannon C. Grabich, Kristen M. Rappazzo, Christine L. Gray, ... Published: 24 October 2016
Frontiers in Public Health, doi: 10.3389/fpubh.2016.00232
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000 to 2005. The EQI, a county-level index constructed for the 2000–2005 time period, was constructed from five domain-specific indices (air, water, land, built, and sociodemographic) using principal component analyses. County-level preterm birth rates (n = 3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PDs) and 95% confidence intervals (CIs) comparing worse environmental quality to the better quality for each model for (a) each individual domain main effect, (b) the interaction contrast, and (c) the two main effects plus interaction effect (i.e., the “net effect”) to show departure from additivity for the all U.S. counties. Analyses were also performed for subgroupings by four urban/rural strata. We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interactions, between the sociodemographic/air domains [net effect (i.e., the association, including main effects and interaction effects) PD: −0.004 (95% CI: −0.007, 0.000), interaction contrast: −0.013 (95% CI: −0.020, −0.007)] and built/air domains [net effect PD: 0.008 (95% CI 0.004, 0.011), interaction contrast: −0.008 (95% CI: −0.015, −0.002)]. Most interactions were between the air domain and other respective domains. Interactions differed by urbanicity, with more interactions observed in non-metropolitan regions. Observed antagonistic associations may indicate that those living in areas with multiple detrimental domains may have other interfering factors reducing the burden of environmental exposure. This study is the first to explore interactions across different environmental domains and demonstrates the utility of the EQI to examine the relationship between environmental domain interactions and human health. While we did observe some departures from additivity, many observed effects were additive. This study demonstrated that interactions between environmental domains should be considered in future analyses.
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