A Statistical Calibration Framework for Improving Non-Reference Method Particulate Matter Reporting: A Focus on Community Air Monitoring Settings

Recent advancement in lower-cost air monitoring technology has resulted in an increased interest in community-based air quality studies. However, non-reference monitoring (NRM; e.g., low-cost sensors) is imperfect and approaches that improve data quality are highly desired. Herein, we illustrate a framework for adjusting continuous NRM measures of particulate matter (PM) with field-based comparisons and non-linear statistical modeling as an example of instrument evaluation prior to exposure assessment. First, we collected continuous measurements of PM with a NRM technology collocated with a US EPA federal equivalent method (FEM). Next, we fit a generalized additive model (GAM) to establish a non-linear calibration curve that defines the relationship between the NRM and FEM data. Then, we used our fitted model to generate calibrated NRM PM data. Evaluation of raw NRM PM2.5 data revealed strong correlation with FEM (R = 0.9) but an average bias (AB) of −2.84 µg/m3 and a root mean square error (RMSE) of 2.85 µg/m3, with 406 h of data. Fitting of our GAM revealed that the correlation structure was maintained (r = 0.9) and that average bias (AB = 0) and error (RMSE = 0) were minimized. We conclude that field-based statistical calibration models can be used to reduce bias and improve NRM data used for community air monitoring studies

A Statistical Calibration Framework for Improving Non-Reference Method Particulate Matter Reporting: A Focus on Community Air Monitoring Settings

Recent advancement in lower-cost air monitoring technology has resulted in an increased interest in community-based air quality studies. However, non-reference monitoring (NRM; e.g., low-cost sensors) is imperfect and approaches that improve data quality are highly desired. Herein, we illustrate a framework for adjusting continuous NRM measures of particulate matter (PM) with field-based comparisons and non-linear statistical modeling as an example of instrument evaluation prior to exposure assessment. First, we collected continuous measurements of PM with a NRM technology collocated with a US EPA federal equivalent method (FEM). Next, we fit a generalized additive model (GAM) to establish a non-linear calibration curve that defines the relationship between the NRM and FEM data. Then, we used our fitted model to generate calibrated NRM PM data. Evaluation of raw NRM PM2.5 data revealed strong correlation with FEM (R = 0.9) but an average bias (AB) of −2.84 µg/m3 and a root mean square error (RMSE) of 2.85 µg/m3, with 406 h of data. Fitting of our GAM revealed that the correlation structure was maintained (r = 0.9) and that average bias (AB = 0) and error (RMSE = 0) were minimized. We conclude that field-based statistical calibration models can be used to reduce bias and improve NRM data used for community air monitoring studies.</jats:p

Sensitivity of Mouse Lung Nuclear Receptors to Electronic Cigarette Aerosols and Influence of Sex Differences: A Pilot Study

The emerging concern about chemicals in electronic cigarettes, even those without nicotine, demands the development of advanced criteria for their exposure and risk assessment. This study aims to highlight the sensitivity of lung nuclear receptors (NRs) to electronic cigarette e-liquids, independent of nicotine presence, and the influence of the sex variable on these effects. Adult male and female C57BL/6J mice were exposed to electronic cigarettes with 0%, 3%, and 6% nicotine daily (70 mL, 3.3 s, 1 puff per min/30 min) for 14 days, using the inExpose full body chamber (SCIREQ). Following exposure, lung tissues were harvested, and RNA extracted. The expression of 84 NRs was determined using the RT2 profiler mRNA array (Qiagen). Results exhibit a high sensitivity to e-liquid exposure irrespective of the presence of nicotine, with differential expression of NRs, including one (females) and twenty-four (males) in 0% nicotine groups compared to non-exposed control mice. However, nicotine-dependent results were also significant with seven NRs (females), fifty-three NRs (males) in 3% and twenty-three NRs (female) twenty-nine NRs (male) in 6% nicotine groups, compared to 0% nicotine mice. Sex-specific changes were significant, but sex-related differences were not observed. The study provides a strong rationale for further investigation

Older African Immigrants’ Experiences of Discrimination in the United States

Abstract Discrimination is implicated in the disproportionate burden of disease and health disparities in racial/ethnic minorities. This qualitative descriptive study explored the experiences of discrimination and its impact on the health of older African immigrants. Semi-structured interviews were conducted with 15 participants. Three main themes and six sub-themes were identified. These included: 1) types of discrimination: a) accent-based, b) unfair treatment during routine activities, c) experience with police and other systems; 2) costs of discrimination; 3) surviving and thriving with discrimination: a) “blind eye to it”, b) reacting to it, c) avoiding it. These themes describe common forms of discrimination that these older adults have experienced, current strategies used to deal with discrimination, and the impact of discrimination on the wellbeing of this sample. To improve the emotional and mental health of older African immigrants, providers serving them should assess for perceived discrimination, and refer participants with any concerns for treatment.</jats:p

Comparison of Cardiovascular Disease Risk Factors Among African Immigrants and African Americans: An Analysis of the 2010 to 2016 National Health Interview Surveys

Background Racial/ethnic minorities, especially non‐Hispanic blacks, in the United States are at higher risk of developing cardiovascular disease. However, less is known about the prevalence of cardiovascular disease risk factors among ethnic sub‐populations of blacks such as African immigrants residing in the United States. This study's objective was to compare the prevalence of cardiovascular disease risk factors among African immigrants and African Americans in the United States. Methods and Results We performed a cross‐sectional analysis of the 2010 to 2016 National Health Interview Surveys and included adults who were black and African‐born (African immigrants) and black and US ‐born (African Americans). We compared the age‐standardized prevalence of hypertension, diabetes mellitus, overweight/obesity, hypercholesterolemia, physical inactivity, and current smoking by sex between African immigrants and African Americans using the 2010 census data as the standard. We included 29 094 participants (1345 African immigrants and 27 749 African Americans). In comparison with African Americans, African immigrants were more likely to be younger, educated, and employed but were less likely to be insured ( P &lt;0.05). African immigrants, regardless of sex, had lower age‐standardized hypertension (22% versus 32%), diabetes mellitus (7% versus 10%), overweight/obesity (61% versus 70%), high cholesterol (4% versus 5%), and current smoking (4% versus 19%) prevalence than African Americans. Conclusions The age‐standardized prevalence of cardiovascular disease risk factors was generally lower in African immigrants than African Americans, although both populations are highly heterogeneous. Data on blacks in the United States. should be disaggregated by ethnicity and country of origin to inform public health strategies to reduce health disparities. </jats:sec