Browsing by Author "Peel, Jennifer, advisor"
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Item Open Access Association between exposure to cadmium and lead during gestation and adverse birth outcomes in the household air pollution intervention network (HAPIN) trial(Colorado State University. Libraries, 2024) Alhassan, Mohamed Adnan, author; Peel, Jennifer, advisor; Clark, Maggie, committee member; Keller, Kayleigh, committee member; Neophytou, Andreas, committee memberLow- and middle-income countries (LMICs) are particularly vulnerable to the adverse effects of metal exposure. These countries' rapid industrialization coupled with population growth, result in substantial environmental exposures, which many governments have limited capacity to formally regulate. Even when regulations exist, many governments have a limited capacity to enforce those regulations. Additionally, LMICs bear a disproportionate burden of adverse birth outcomes, including low birth weight and preterm birth, which carry long-term health implications such as increased risk of chronic diseases, developmental delays, and mortality. Several studies have examined the association between metals and adverse birth outcomes such as low birth weight and preterm births. Specifically, despite the low number of studies, cadmium has been consistently linked to lower birth weights, smaller sizes for gestational age, and reduced head circumference. However, the association between lead exposure and birth outcomes shows inconsistent results. This inconsistency in findings, along with the low number of studies overall, especially in LMICs, regarding lead has prompted further investigation in our current study. Here we utilized data from the Household Air Pollution Intervention Network (HAPIN) trial, a randomized controlled trial conducted in rural areas of Guatemala, Peru, Rwanda, and India. The HAPIN trial evaluated the impact of replacing biomass stoves with liquefied petroleum gas stoves on various health outcomes, including infant birth weight among 3200 participants. The participants in the current analysis included pregnant women with a live singleton birth with exposure and birth data (n=2396). Maternal exposure to cadmium and lead were evaluated by analyzing dried blood spots using inductively coupled mass spectrometry. Blood spots were collected at baseline (9 - <20 weeks gestational age) and 32-36 weeks gestational age; we also evaluated the average of these two measurements. Birth weight was measured using a digital infant scale, with low birth weight defined as <2500 grams, and gestational age at birth was determined using screening data and ultrasonography, with preterm birth defined as <37 weeks. We utilized linear regression for birth weight and gestational age, logistic regression for dichotomous low birth weight, and Cox proportional hazards model for preterm birth. The models accounted for infant sex, maternal age, nulliparity, body mass index, maternal hemoglobin, mother's dietary diversity, food insecurity, tobacco smoking in the household, and study arm. We assessed effect modification by study location, sex, and study arm by including an interaction term. In sensitivity analyses, we included study location, household assets, maternal education in the models; replaced values below the limits of detection (LOD) with LOD/√2, and evaluated metal concentrations standardized by potassium levels. We also excluded maternal hemoglobin from the main model. The mean birth weight was 3,020 (standard deviation [SD]=445.5) grams, and 10.3% of all births were classified as low birth weight. The mean gestational age was 39.5 weeks (SD=1.7 weeks), and 5.2% of the births were preterm. The median lead concentration across the time points was 1.4 μg/dL (IQR: 0.9 – 2.2 μg/dL), and the median cadmium concentration was 1.0 ng/mL (IQR: 0.7 – 1.4 ng/mL), values comparable to those found in other studies. Overall, the results did not indicate a consistent or strong association between lead or cadmium and adverse birth outcomes. Baseline cadmium levels showed a modest increase in the odds ratio for low birth weight (OR per IQR increase: 1.2, 95% CI: 0.97 to 1.47). Sensitivity analyses closely aligned with the main findings. All the results for effect modification did not indicate differences in the strata. The study found a suggestive, but inconsistent evidence between exposure to cadmium and low birth weight. This study has some limitations. There is potential for non-differential measurement error due to the hematocrit effect, which alters the estimated spot volume based on participants' hematocrit levels. A sensitivity analysis using potassium standardized metal concentrations partially addressed this, but individual hematocrit variability can still bias the observed association towards the null, with a moderate magnitude. The probability of the bias is moderate. The chromatographic effect, which can cause variations in concentration due to the interaction between blood and the analyte with the filter paper, was also partially addressed using internal standards, blanks, calibration samples, quality controls, and reference materials. This potential bias is of low probability and magnitude, biasing the observed association toward the null. Confounding bias was considered a concern due to incomplete adjustment for covariates like seasonal variation, which can affect metal exposure and birth outcomes. Sensitivity analyses supported the main model findings, suggesting a low probability and magnitude of confounding bias, which could bias the observed association towards or away from the null. Despite residual confounding concerns linked to socio-economic indicators like assets and diet diversity, the sensitivity analyses did not deviate from the main model findings, indicating a small probability and magnitude of the bias, which would bias the observed association in either direction. The study had several strengths including a large sample size compared to previous studies, especially those in LMICs and it was conducted in three distinct rural LMIC settings, which, to the best of our knowledge, had not been done before. This study's strength lies in its large sample size of 2,152 participants with complete data, enhancing its statistical robustness and addressing the common issue of small sample sizes and missing data in prior LMIC research. Additionally, its unique examination across three distinct rural LMIC settings provides valuable insights into the regional variations affecting the outcomes studied. Future steps include using whole blood samples instead of dried blood spots (DBS) and measuring exposure at multiple time points, particularly at birth via the umbilical cord, could yield more accurate concentrations. It is also recommended that subsequent studies employ better socio-economic indicators to reduce residual confounding effects. Expanding the geographical scope of the study to include a broader range of urban areas within the HAPIN countries would improve the generalizability of the findings. Additionally, future research should consider analyzing the effects of metal mixtures to better replicate real-world environmental conditions and interactions. The results are generally consistent with existing limited data indicating no evidence of an association between lead and adverse birth outcomes and a potential association between higher cadmium exposure during pregnancy with increased risk of low birth weight.Item Open Access Associations between air pollution emitted from cookstoves and central hemodynamics, arterial stiffness, and blood lipids in laboratory and field settings(Colorado State University. Libraries, 2019) Walker, Ethan Sheppard, author; Peel, Jennifer, advisor; Clark, Maggie, advisor; Dinenno, Frank, committee member; Volckens, John, committee member; Wilson, Ander, committee memberTo view the abstract, please see the full text of the document.Item Open Access Examination of the complex relationships among dietary components, type II diabetes, weight change, and breast cancer risk among Singaporean Chinese women(Colorado State University. Libraries, 2015) Canales, Lorena Lea, author; Peel, Jennifer, advisor; Clark, Maggie, committee member; Bachand, Annette, committee member; Nelson, Tracy, committee member; Ryan, Elizabeth, committee memberType II diabetes and breast cancer are on the rise in Asian populations that have typically had lower burdens of disease. Intake of dietary components high in nutrients with anti-oxidative and anti-inflammatory properties, such as green tea, soy, fruits and vegetables, may protect against the development of type II diabetes and may improve HbA1c (glycated hemoglobin) levels, a clinically relevant biomarker of diabetes and prediabetes. Furthermore, modifiable lifestyle factors such as diabetes, weight change and diet that influence endogenous hormone levels and the insulin pathway may play a role in the development of breast cancer. This dissertation includes three aims that examined different aspects of the complex relationships between diet, diabetes, weight change, and breast cancer risk in the Singapore Chinese Health Study, a prospective cohort study that enrolled 63,257 Chinese men and women aged 45-74 years between 1993 and 1998. First, we examined the association between intake of green tea, soy, and a vegetable-fruit-soy dietary pattern on HbA1c levels among self-reported, nondiabetic men and women, examined separately (Aim 1). We also evaluated type II diabetes and weight change (separately) in relation to risk of breast cancer, as well as the potential interaction of diet (soy and green tea intake) with the exposures of interest among women only (Aims 2 and 3). Dietary intake was assessed at baseline (1993-1998) by in-person interviews using a validated 165-item food frequency questionnaire. HbA1c levels were measured from blood samples collected in the follow-up period after baseline enrollment (1999-2004), and self-reported diabetes diagnosis was determined at the follow-up interview. Self-reported weights at the baseline and follow-up interviews were used to determine weight change. Multivariable linear regression (Aim 1) and proportional hazards regression models (Aims 2 and 3) were used to evaluate these associations. In Aim 1, adjusted mean HbA1c levels were inversely related to soy protein intake (p-value = 0.02; p for trend across the four quartiles of soy protein intake = 0.05) among women; the mean HbA1c difference between the highest and lowest quartile of soy protein intake of 0.07%. We also observed higher HbA1c levels for women with higher green tea intake (p for trend of 0.11), which was in the direction opposite to that hypothesized. In Aim 2, we observed a non-statistically significant increase in breast cancer risk among women with type II diabetes (adjusted hazard ratio [HR]=1.24, 95% confidence interval [CI]: 0.82, 1.86). The assessment of the joint effects of diabetes and lower soy isoflavone intake suggested a weak non-significant interaction between these variables on breast cancer risk; the HR for breast cancer was slightly elevated among those with lower soy isoflavone intake, while among those with higher isoflavone intake the HR was consistent with a null association. There was no evidence of interaction when evaluating soy food, soy protein and green tea intake on the diabetes and breast cancer association. In Aim 3, we did not observe evidence of an increase in breast cancer risk among women reporting weight gain between baseline and follow-up interviews; however, we observed an increase in risk among women who lost between 3 and 5 kilograms between baseline and follow-up interviews (HR=1.31, 95% CI: 0.94, 1.83), which was in the direction opposite of what was hypothesized. This result was similar when we removed breast cancer cases diagnosed within the first two years of follow-up. There was no evidence of interaction between weight change and soy and green tea intake. In conclusion, we provide suggestive evidence that soy protein intake is associated with decreased HbA1c levels among self-reported nondiabetic women. Furthermore, our results suggest that soy isoflavone intake may weakly modify the association between type II diabetes and breast cancer risk. Collectively, the results of these three studies indicate that soy intake may be protective for the development and progression of type II diabetes and could also attenuate the adverse impact of type II diabetes on breast cancer risk. However, given that these results are suggestive for different soy components and the short follow-up time of the prospective evaluation of breast cancer risk, further research is needed to investigate this question. Furthermore, research among populations with varying levels of soy intake is also needed to assess these associations.Item Open Access Impact of a cookstove intervention on exposure and blood pressure in rural Honduran women(Colorado State University. Libraries, 2018) Heiderscheidt, Judy Marie, author; Peel, Jennifer, advisor; Keefe, Thomas, advisor; Clark, Maggie, committee member; Magennis, Ann, committee member; Stallones, Lorann, committee memberTo view the abstract, please see the full text of the document.Item Open Access Impact of an improved stove intervention on exposure and health among Nicaraguan women(Colorado State University. Libraries, 2014) Yoder, Sarah, author; Peel, Jennifer, advisor; Bachand, Annette, advisor; Nelson-Ceschin, Tracy L., committee memberTo view the abstract, please see the full text of the document.Item Open Access Indoor air pollution from cookstove smoke and adverse health effects among Honduran women(Colorado State University. Libraries, 2007) Clark, Maggie L., author; Peel, Jennifer, advisorElevated indoor air pollution exposures associated with the burning of biomass fuels in developing countries are well established. Several studies have demonstrated the value of estimating exposures by evaluating stove type, personal cooking practices, and household parameters. Adverse health endpoints have been associated with cookstove exposures, although little research has been performed on cardiovascular health endpoints in these settings. We conducted a cross-sectional survey among 79 non-smoking Honduran women. Thirty-eight women cooked with traditional stoves and 41 with improved stoves with chimneys. For a subgroup of these women (N=54-58), carbon monoxide and particulate matter (PM2.5) levels were assessed via eight-hour indoor monitoring, as well as eight-hour personal PM2.5 monitoring. Stove quality was assessed using a four-level subjective scale representing the potential for indoor emissions. The stove scale and ventilation factors predicted more than 50% of the variation in personal and indoor PM2.5 and 85% of the variation in indoor carbon monoxide. In addition to the stove scale, other factors predicting exposure measurements were the age of the stove, the total area of the kitchen windows, the number of kitchen walls, the primary material of the kitchen walls, the volume of the kitchen, and the number of walls with eave spaces. Forced expiratory volume in one second and peak expiratory flow, as well as respiratory symptoms and demographic characteristics were assessed. Finger-stick blood samples were collected and dried on filter paper in order to assess a biomarker of inflammation, C-reactive protein (CRP). Women exposed to cookstove exposures reported symptoms of cough, phlegm, wheeze, headache, and shortness of breath more frequently than those not exposed. Associations consistent with a null association were observed between cookstove exposures and lung function and CRP. Results of the exposure assessment could provide a cost-effective alternative to air quality monitoring. The ease and convenience of collecting, storing, and transporting finger-stick blood samples, could prove to be a useful tool for larger community-based investigations, especially in developing countries.Item Open Access Public health considerations for a potential Lyme disease vaccine in the United States: cost of illness, vaccine acceptability, and net costs of a vaccination program(Colorado State University. Libraries, 2021) Hook, Sarah A., author; Peel, Jennifer, advisor; Anderson, Brooke, committee member; Bayham, Jude, committee member; Magzamen, Sheryl, committee memberTo view the abstract, please see the full text of the document.Item Open Access Spatial analysis of human Lyme disease risk in an endemic county(Colorado State University. Libraries, 2014) Kugeler, Kiersten Jenae, author; Peel, Jennifer, advisor; Reif, John, committee member; Eisen, Lars, committee member; Mead, Paul, committee member; Biggerstaff, Brad, committee memberAn understanding of the factors that drive spatial variation in human Lyme disease risk is important for appropriate development and implementation of public health interventions. Yet, these factors are poorly understood. This dissertation utilized fine-scale environmental and human Lyme disease data from a single county to quantify the spatial distribution of human Lyme disease occurring 2001-2011 and to evaluate whether spatial variation in disease risk was explained by several factors, including land use, land cover, deer density, and tick infestation on deer. All studies were conducted with data from Howard County, Maryland. The first project described spatial clustering of human Lyme disease according to residence. When compared to other areas of the County, areas with elevated disease risk were characterized by more low-density development and more red and white oak forest. The second project used multilevel (i.e., mixed effect) models to examine risk factors for human Lyme disease among all homes in Howard County. In this analysis, 8% of all variation in human disease risk was due to the census block group location of households; the remaining variation in human disease risk occurred within census block groups. Most of the variation in risk between census block groups was explained by household-level land use and land cover characteristics and census block group-level differences in forest and socio-demographics, yet some variation in risk between block groups remained unexplained with available covariates. Increased risk of Lyme disease was associated with low- and medium-density residential development, red and white oak forest, increasing proportion of the census block group classified as forest, and residing in a census block group characterized by higher income, home value, and education. The third project evaluated associations between deer density, tick infestation on deer, and human disease risk. Study findings suggested that areas with lower deer density had higher abundance of ticks on deer and higher risk of human Lyme disease. These results suggest that moderate deer reduction in inland areas, as occurs through community deer management programs, may not be a viable Lyme disease prevention measure. This dissertation advances knowledge of the fine-scale epidemiology of human Lyme disease and demonstrates the importance of using human outcome data, in addition to entomologic data, to understand variation in Lyme disease risk. These studies use advanced analytic methods to demonstrate significant sub-county spatial variation in risk of human Lyme disease, validate previously recognized risk factors for human illness, identify novel associations of a specific forest type with human disease, and demonstrate the importance of human behavior in placing humans at risk. Finally, results of this dissertation suggest that additional analyses using multilevel modeling techniques may help to provide insight regarding many remaining questions in the epidemiology of Lyme disease.Item Open Access The association between political environment and COVID-19 mortality in selected Colorado counties(Colorado State University. Libraries, 2023) DeBie, Kelly, author; Neophytou, Andreas, advisor; Peel, Jennifer, advisor; Gutilla, Molly, committee member; Rojas-Rueda, David, committee memberThe SARS-CoV-2 virus spread worldwide triggering a global Coronavirus (COVID-19) pandemic. COVID-19 remains a public health threat today and may continue to do so into the future dependent on the emergence of variants and our ability to mitigate harm through vaccines and other public health measures. The COVID-19 pandemic struck the United States during a time of great political tension and divide under the administration of President Donald Trump. State-level variation in mitigation measures may have been influenced by political views. COVID-19 mortality rates also varied by county. This paper seeks to investigate whether the county-level political environment was associated with differences in COVID-19 mortality in the state of Colorado. We examined the association between political environment and county-level age-adjusted COVID-19 mortality rates during 2020 and 2021. Political environment is measured using data from the 2016 and 2020 Presidential election vote distribution by county, obtained from the Colorado Secretary of State. Outcome data was obtained from the Colorado Department of Public Health and Environment (CDPHE), having already been age-adjusted using direct standardization based on the 2010 Census. Any counties with 3 of fewer deaths in a calendar year were excluded, leaving a total of 48 counties in 2020 and 56 in 2021. Rate ratios and 95% confidence intervals were estimated using Quasi-Poisson regression models, separately for 2020 and 2021 mortality data. The models were adjusted for population density, the percentage of county residents without health insurance, and the demographics percentile from the Colorado EnviroScreen Environmental Justice Tool. Models were further evaluated for the presence of effect modification by population density. There are a total of 64 counties in the state of Colorado. In the 2016 election, 42 counties voted for Donald Trump. In the 2020 election, that dropped to 40 counties. Age-adjusted mortality rates ranged from 14.3-458.0 per 100,000 over the two years of data. For 2021 mortality data, the estimated mean adjusted mortality rate was 78% higher among counties where aggregated individual votes were highest in percentage for Donald Trump in 2016 as compared to counties with highest vote percentage for Hilary Clinton. (RR = 1.78; 95% CI: 1.26-2.59). For 2020, the estimated mean adjusted mortality rate was found to be 24% higher among counties voting in highest percentage for Donald Trump in 2016 as compared to counties voting in highest percentage for Hilary Clinton, though this association was not statistically significant. (RR=1.24; 95% CI: 0.81-1.94). Similar results were observed for the 2020 election data (comparing county-level voting results for Trump vs. Biden). We did not observe evidence that the association was modified by population density. This study observed an association between county-level political environment and age-adjusted COVID-19 mortality rates, specifically finding an association that became statistically significant during the pandemic. These results build on a growing body of evidence studying the links between politics and COVID-19 outcomes. Strengths of this study include the use of publicly available datasets, state-wide analysis, multiple model options with similar results indicating robustness, and utilization of a novel environmental justice metric to adjust for multiple confounders simultaneously. As this was an ecological study, inference cannot be extended to individuals. Future research may want to further explore both the individual and community political exposures that may influence mortality. It may also be suggested to investigate election data as a continuous rather than binary variable to tease out the relationship in more detail. Studies such as this may be useful as the COVID-19 pandemic is still ongoing, and in preparation for any future pandemics.