NEHA May 2024 Journal of Environmental Health

The May 2024 issue of the Journal of Environmental Health (Volume 86, Number 9), published by the National Environmental Health Association.

JOURNAL OF Environmental Health Dedicated to the advancement of the environmental health professional Volume 86, No. 9 May 2024

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ADVANCEMENT OF THE SCIENCE The Impact of Poverty Status on Blood Lead Levels Among Individuals in the United States From 2017–2018: An Analysis of the National Health and Nutrition Examination Survey .................................................................................................................... 8 Incorporating Novel Methods Into a Standard Environmental Legionnaires’ Disease Investigation and Identifying the Exposure Source of an Outbreak in New York ....................16

ABOUT THE COVER

Globally, food- borne illness is a significant public health challenge. Food safety inspec- tion plays a crucial role in regulating food businesses to prevent foodborne illnesses. This

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month’s cover article examined how food safety inspectors from various countries assess food contamination control during inspections. The findings indicate that inspectors are gener- ally aware of food safety hazards and deploy various relevant data-gathering methods. The findings also indicated a prevailing method- ological incongruence stemming from the absence of a robust inspection methodology. The authors propose the development of a clear and appropriate methodology to support food safety inspections to provide a robust and reliable means for evaluating food safety risk and reducing the incidence and burden of foodborne illness. See page 24. Cover images © iStockphoto: SolStock, sopradit

International Perspectives: Examining Food Safety Inspections: Do They Meet the Grade to Protect Public Health? ............................................................................................. 24

Direct From AAS: The Home Food Processing Establishment License in Iowa ............................. 36

Direct From CDC/Environmental Health Services: Engaging With the Model Aquatic Health Code at the Local Level: Tools and Resources for Users From the National Association of County and City Health Ocials .......................................................................... 38 Direct From U.S. EPA/Oce of Research and Development: Cyanobacterial Harmful Algal Blooms and Public Health: Using Tools From the Cyanobacteria Assessment Network to Reduce Exposure ..................................................................................................................... 42

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JEH Quiz #6............................................................................................................................... 45

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President’s Message: Moving the Profession Forward .............................................................................. 6

Special Listing ........................................................................................................................... 48

NEHA 2024 AEC....................................................................................................................... 50

NEHA News .............................................................................................................................. 52

NEHA Member Spotlight .......................................................................................................... 54

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in the next Journal of Environmental Health don’t miss h Developing Environmental Health and Land Reuse Trainings for the Environmental Health Workforce and Their Community Partners h Performance Indicators Corresponding to the Critical Competencies in Children’s Environmental Health h Survey of Minnesota Food Workers About Information Sharing Preferences, Inspection Perceptions, and Employee Illness Behavior

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An open access journal published monthly (except bimonthly in January/ February and July/August) by the National Environmental Health Association (NEHA), 720 S. Colorado Blvd., Suite 105A, Denver, CO 80246-1910. Phone: (303) 802-2200; Internet: www.neha.org. E-mail: jeh@neha.org. Volume 86, Number 9. Yearly print subscription rates: $160 (U.S.) and $200 (international). Single print copies: $15, if available. Claims must be filed within 30 days domestic, 90 days foreign, © Copyright 2024, NEHA (no refunds). Opinions and conclusions expressed in articles, columns, and other contributions are those of the authors only and do not reflect the policies or views of NEHA. NEHA and the Journal of Environmental Health are not liable or responsible for the accuracy of, or actions taken on the basis of, any information stated herein. NEHA and the Journal of Environmental Health reserve the right to reject any advertising copy. Advertisers and their agencies will assume liability for the content of all advertisements printed and also assume responsibility for any claims arising therefrom against the publisher. Advertising rates available at www.neha.org/jeh. The Journal of Environmental Health is indexed by Clarivate, EBSCO (Applied Science & Technology Index), Elsevier (Current Awareness in Biological Sciences), Gale Cengage, and ProQuest. The Journal of Environmental Health is archived by JSTOR (www.jstor.org/journal/ jenviheal). Full electronic issues from present to 2012 available at www.neha.org/jeh. All technical manuscripts submitted for publication are subject to peer review. Visit www.neha.org/jeh for submission guidelines and instructions for authors. To submit a manuscript, visit https://jeh.msubmit.net. Direct all questions to jeh@neha.org. Periodicals postage paid at Denver, Colorado, and additional mailing offices. POSTMASTER: Send address changes to Journal of Environmental Health , 720 S. Colorado Blvd., Suite 105A, Denver, CO 80246-1910.

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May 2024 • Journal of Environmental Health

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Open Access

 PRESIDENT’S MESSAGE

Moving the Profession Forward

Tom Butts, MSc, REHS

A s environmental public health prac- titioners, registered sanitarians, or whatever associated job title you hold, we operate at the forefront of public health to safeguard communities from envi- ronmental hazards and promote well-being through the enforcement of health standards and regulations. We also work to ensure our communities are implementing best practic- es and model codes. Further, we apply our problem-solving abilities to unique situations as needed. As guardians of public health, we are tasked with a critical balance—integrat- ing new technologies and scientific advance- ments into our practices while honoring the time-tested methods and lessons of the past. This narrative explores how we can navigate this dynamic landscape to blend innovation and data-driven knowledge with tradition to enhance environmental health. The dawn of the 21st century has ush- ered in an era marked by rapid technologi- cal advancements and significant scientific breakthroughs. In the realm of environmen- tal health, these developments present an unprecedented opportunity to elevate the ecacy and reach of health initiatives. From advanced data analytics to cutting-edge bio- technologies and low-cost (relatively speak- ing) air, water, and other environmental monitors, the tools at our disposal are power- ful and diverse. The integration of these tools into the fabric of environmental health prac- tice, however, requires a nuanced approach that respects the foundational principles of the field while striving for progress. Historically, environmental health has been shaped by the lessons learned from public health triumphs and tragedies. The

emerging science and technology as tools to enhance, rather than replace, the core prac- tices of environmental health. One of the most promising areas for tech- nological integration is the collection and analysis of environmental data. Advanced sensors and remote monitoring technolo- gies now enable us to track environmental conditions in real-time, identifying potential health hazards with unprecedented speed and accuracy. Leveraging these technologies eectively, however, requires a deep under- standing of the environmental determinants of health, a knowledge base that is rooted in both scientific research and historical data. By combining these new data streams with traditional epidemiological methods, envi- ronmental health professionals can develop more nuanced and eective interventions. Another frontier is the application of bio- technology in environmental health. Inno- vations in genetic engineering and micro- biology oer new avenues for addressing environmental health challenges, from biore- mediation of contaminated sites to the devel- opment of novel vaccines. The application of these technologies must, however, be guided by ethical considerations and a precautionary approach that takes into account the poten- tial long-term impacts on communities, eco- systems, and human health. Here again, the lessons of history serve as a valuable guide, reminding us of the importance of thorough risk assessment and the potential conse- quences of intervention in natural systems. In navigating the integration of new or emerging technologies (including artificial intelligence), communication and collabora- tion emerge as critical themes. Environmen-

Through collaboration, innovation, and a steadfast commitment to the principles of

public health, we can navigate the complexities of the 21st century.

devastating outbreaks of the past taught us the importance of sanitation, the value of vaccination, and the critical need for pub- lic education on health matters. These les- sons, hard-earned over centuries, form the bedrock of current best practices in the field. We are the custodians of this legacy, apply- ing the insights gleaned from history to the challenges of the present. As we embark on the path of technologi- cal integration, it is essential to recognize that innovation is not a panacea. The allure of new technology can sometimes overshadow the fundamental principles of environmental health, leading to solutions that are techno- logically sophisticated but practically flawed. I have witnessed several iterations of tech- nology emerge that can provide data beyond our ability to interpret those data relative to existent or nonexistent standards. Possessing a collection of data before you have a path to guide responses to the findings is an awk- ward place to be. The key, then, is to embrace

Volume 86 • Number 9

tal health professionals must engage with a wide range of partners, from scientists and technologists to policymakers and the pub- lic. This dialogue is essential for ensuring that technological innovations are grounded in the realities of public health practice and are responsive to the needs and concerns of the many diverse components and interests of communities. Moreover, education and training play a pivotal role in preparing the next genera- tion of environmental health professionals. By starting with a foundation in science and incorporating emerging sciences and technol- ogies into the curriculum, while also empha- sizing the importance of historical lessons and ethical considerations, educational insti- tutions can equip students with the skills and perspective needed to navigate the complex landscape of modern environmental health.

In addition to the science foundation, robust communication skills need to be developed and regularly refreshed. The future of environmental public health lies in a balanced approach that honors the wisdom of the past while boldly embracing the possibilities of the future. By integrating new technologies, scientific advancements, innovative approaches with respect for cur- rent best practices, and historical lessons learned, we can enhance our ability to pro- tect public health and the environment. We can find eective, ecient, and even elegant ways to address environmental public health issues, such as what former Colorado Gov- ernor John Hickenlooper used in his Dash- board (Ely et al., 2019). This journey—though fraught with chal- lenges—holds the promise of a healthier, more resilient world where the achievements

of science and technology are harnessed for the common good. Through collaboration, innovation, and a steadfast commitment to the principles of public health, we can navi- gate the complexities of the 21st century and safeguard the well-being of communities for generations to come. Reference Ely, T.L., Teske, P., & Swann, W.L. (2019). Public display of performance: The Governor’s Dashboard in Colorado (Report no. 18-08E). School of Public Aairs, University of Colo- rado Denver. https://coloradolab.org/wp- content/uploads/2019/11/Report-4-Gover nors-Dashboard.11.1.19.pdf tbutts@neha.org

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May 2024 • Journal of Environmental Health

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Open Access

The Impact of Poverty Status on Blood Lead Levels Among Individuals in the United States From 2017–2018: An Analysis of the National Health and Nutrition Examination Survey b:;r*,; This cross-sectional study examines the population prevalence of blood lead levels (BLLs) greater than the sample median of 0.76 μg/dl in the U.S. population and investigates demographic factors as- sociated with higher BLLs using data from the 2017–2018 National Health and Nutrition Examination Survey. Logistic regression models were used to assess the impact of poverty status on BLLs, with adjustments for factors such as race, age, gender, and lifetime smoking history. This study found that individuals living in or near poverty had significantly higher odds of having a BLL >0.76 μg/dl when compared with individuals not in poverty, with an adjusted OR of 1.824 (95% CI [1.324, 2.513]) and 1.744 (95% CI [1.388, 2.190]), respectively. This nationally representative study has important implications for understanding the demographic and socioeco- nomic factors that aect BLLs in the U.S. population. Keywords: blood lead level, lead, poverty, environmental justice, environ- ment, regression

Michael Ricciardi, MPH

with a lower economic status, it is reason- able to assume that there is greater exposure to lead in individuals of lower income brack- ets. Moreover, dierences in BLL by ethnic- ity, gender, and age have been described, with Black children having the highest BLLs on average (Bernard & McGeehin, 2003; Kurtin et al., 1997). Additionally, research has dem- onstrated increased BLLs in individuals who smoke cigarettes (Shaper et al., 1982). When considering the health eects of lead, it is widely understood that lead expo- sure, even at moderate levels, has detrimen- tal eects on multiple body systems (ATSDR, 2020). The most commonly understood eect of lead exposure is likely a decrease in cognitive function. Research has shown a significant negative association between lead poisoning and social, behavioral, and intel- lectual development (Hou et al., 2013). Fur- ther, a literature review by Vorvolakos et al. (2016) associates lead with the development of schizophrenia, anxiety, and depression. Along with this finding, research has shown that lead exposure in childhood results in impaired cognitive development that can persist over time, as well as increased risks for Alzheimer’s disease and other degen- erative brain conditions (Koshy et al., 2020; Peters et al., 2010; Reuben, 2018). Aside from its impact on cognition, lead exposure has also been implicated in a myr- iad of diseases and conditions not related to cognition. For one, maternal BLLs and cord BLLs have shown a strong, significant nega- tive association with birthweight (Wang et al., 2020), and there is evidence that mater- nal BLL negatively impacts fetal ossifica- tion (Saleh et al., 2009).A dose-dependent relationship between lead and bone mineral density has also been described, with sig- nificant decreases in bone mineral density

Introduction Lead is a toxic heavy metal that has been recognized for many decades as a significant environmental and public health hazard. Lead is present in the environment as a naturally occurring metal in ore deposits around the world, but these sources of lead do not pose much of a threat to human health. The pub- lic health concern for lead exposure arises when considering the various anthropogenic sources of lead, which include paints, pipes, batteries, solder, pesticides, and lead smelter- ies. Before 1995, when leaded gasoline was still in use, its combustion was a major source of lead exposure for people in the U.S. (Agency for Toxic Substances and Disease Registry [ATSDR], 2020). Of note is the concern for atmospheric deposition of lead into soil, which can contribute to exposure from nonlo- cal sources (ATSDR, 2020). A pooled analysis

concluded that lead-contaminated house dust is the major source of lead exposure for chil- dren (Lanphear et al., 1998). While lead-based paints are no longer in use, homes built before 1978 continue to carry a risk of the deposition of lead into household dust due to paint dete- rioration (ATSDR, 2020). Many factors that increase the risk of exposure to lead have been identified. For example, children living in low- and middle- income countries have been found to have a high prevalence of blood lead levels (BLLs) that exceed the 5 μg/dl threshold set by the Centers for Disease Control and Prevention (Ericson et al., 2021). Infants and toddlers have much more frequent hand-to-mouth behavior, which places them at a higher risk of ingesting lead dust in older homes (ATSDR, 2020). As these older homes are more likely to be occupied by individuals

Volume 86 • Number 9

on a review of existing literature, I hypoth- esized that individuals living near and below the poverty line would have greater odds of higher BLLs compared with individuals liv- ing above the poverty line. Additionally, I hypothesized that the eect of the poverty- income ratio would dier by age group.

TABLE 1

Counts by Age, Race, Gender, Poverty Status, and Smoking History From the 2017–2018 National Health and Nutrition Examination Survey

Demographic

Unweighted Count

Weighted Count

Weighted % [95% Confidence Interval]

Methods

Age (years) <18

2,883

70,079,407 30,502,064 43,778,267 39,196,505 38,350,100 46,707,582 37,570,219 11,048,661 34,787,759 23,322,412 189,718,187 38,011,815 35,002,548

22.09 [20.78, 23.41]

Data Source This cross-sectional analysis used data from the National Health and Nutrition Examina- tion Survey (NHANES) from 2017–2018. The NHANES survey employs a complex survey design that includes stratification, clustering, and weighting to ensure that estimates are representative of the U.S. population (Centers for Disease Control and Prevention, 2024). The sample for this study contained 9,604 individuals. Data for BLLs, tobacco use his- tory, demographics (i.e., race, ethnicity, and gender), and income were downloaded and match-merged. Match-merging is used for merging data sets that have one or more com- mon variables where data sets were merged based on the values of the common variables. Measures For this analysis, each continuous BLL was converted into a binary variable, which arbitrarily categorized individuals as either greater than or less than the sample median. Given that there is no safe level of lead that can be quantified (Vorvolakos et al., 2016), this binary classification sought to place indi- viduals into two groups based on BLLs. Poverty classifications were determined based on continuous poverty-income ratio, with ratios of <1.0, 1.0–1.3, and >1.3 corre- sponding to in poverty, near poverty, and not in poverty, respectively. Smoking history was determined by self-reported lifetime use of ≥100 cigarettes, which has been the classifi- cation scheme used by other tobacco usage analyses. The race and ethnicity demographic variable was split into five groups: Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race. Gender was a dichotomous variable with options only of male or female. Age was a continuous variable for individuals between the ages of 6 and 79 years, with individuals >80 years placed into one response group. A categorical age group variable was created

18–24 25–34 35–44 45–54 55–64 65–80

650 816 778 801

9.62 [8.16, 11.07]

13.80 [12.09, 15.51] 12.36 [10.64, 14.07] 12.09 [10.95, 13.22] 14.72 [12.89, 16.56] 11.84 [9.86, 13.82]

1,096 1,010

>80

382

3.48 [2.75, 4.21]

Race

Mexican American

1,298

10.84 [6.74, 14.95]

Other Hispanic

773

7.27 [5.67, 8.86]

Non-Hispanic White Non-Hispanic Black

2,931 2,010 1,692

59.13 [53.56, 64.70] 11.85 [8.37, 15.33] 10.91 [8.36, 13.46]

Other

Gender

Male

4,273 4,431

156,845,117 163,997,604

48.89 [47.24, 50.53] 51.11 [49.47, 52.76]

Female

Poverty status In poverty

1,753

45,167,716 22,629,530 219,591,784

15.72 [13.34, 18.10]

Near poverty Not in poverty

803

7.87 [6.44, 9.31]

5,078

76.41 [74.71, 78.11]

Smoking history* Yes

2,232 3,301

101,651,059 145,502,339

41.13 [37.73, 44.53] 58.87 [55.47, 62.27]

*Lifetime history of smoking ≥100 cigarettes.

present even at a moderate BLL (Cui et al., 2022). The eect of lead exposure on bone health is echoed by findings that demonstrate higher BLLs in individuals with osteoporosis in adulthood (Campbell & Auinger, 2007). Furthermore, lead exposure is associated with liver disease (Cave et al., 2010), kidney dysfunction (ATSDR, 2020), hematological dysfunctions (ATSDR, 2020), asthma (Wang et al., 2017), hypertension (Huang, 2022; Miao et al., 2020; Yan et al., 2022), cancer mortality (Gwini et al., 2012; Rhee et al., 2021; van Bemmel et al., 2011), and even all-cause mortality (Schober et al., 2006; van Bem-

mel et al., 2011). There is also evidence that lead exposure increases the burden of stroke (Zhang et al., 2022). Given that there is grow- ing evidence suggesting that lead exposure is associated with many diseases and conditions, it is of great importance to elucidate the exact risk factors for exposure to this toxic metal. As research continues, further prevention eorts can be targeted to areas of greatest need. As such, this analysis aimed to a) assess the dierence in BLLs among individuals liv- ing below, near, and above the poverty line and b) perform analyses to assess the odds of high lead for dierent subgroups. Based

May 2024 • our6*l o/ 6=2ro6me6;*l e*l;1

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from continuous age, following the standard 8-group classification. BLL data for indi- viduals ≤5 years were not publicly available; therefore, these individuals were excluded from the analysis. Analysis Sample weights, strata, and clusters were pro- vided by NHANES to account for the com- plex sampling design of this survey. Analysis was performed in SAS version 9.4, with con- sideration of complex sampling in all calcula- tions and regression analyses. Weighted population descriptives were cal- culated for poverty groups, cigarette history groups, and other demographic classifications. Additionally, both adjusted and unadjusted logistic regression models were performed to model the odds of a BLL greater than the sam- ple median. Covariates for adjustment were determined based on significant associations and review of existing literature on the subject. Dierences in prevalence of BLL >0.76 μg/dl were assessed for significance using Wald chi- square tests, with p -values <.0500 considered to be significant. Model 1 was unadjusted and simply modeled the odds of a BLL greater than the sample median based on poverty status. Model 2 adjusted for race and ethnicity, gen- der, and age. Model 3 included the 100-ciga- rette history as a covariate. Parameter estimates for these models were considered significant if the Wald chi-square p -value was <.0500. Age-stratified analysis was also performed for the three regression models using the categorical age group vari- able. Parameter estimates for these models were again considered significant at Wald chi-square p -values <.0500. These models were again used to calculate estimates of the change in total log-odds of a BLL >0.76 μg/dl per one unit increase in poverty-income ratio. Age stratification of the fully adjusted model was also performed to derive the change in total log-odds estimate for each age group. For both, p -values <.0500 were considered to be significant. Results Unweighted and weighted counts and per- centages with 95% confidence intervals (CI) by demographic group of the population are displayed in Table 1. Mean BLLs were calcu- lated for each demographic group (Table 2). Individuals >80 years, individuals identify-

TABLE 2

Mean Blood Lead Level by Age, Gender, Race, and Poverty Status From the 2017–2018 National Health and Nutrition Examination Survey

Demographic

Blood Lead Level (μg/dl)

Mean

Minimum, Maximum

Age (years) <18

0.523 0.664 0.806 1.012 1.055 1.330 1.429 1.732 1.087 0.736 0.994 1.047 1.176

0.017 0.045 0.038 0.100 0.052 0.046 0.041 0.075 0.143 0.029 0.031 0.023 0.050

0.05, 6.13

18–24 25–34 35–44 45–54 55–64 65–80

0.05, 17.02

0.09, 8.82

0.10, 42.48 0.12, 25.27 0.15, 14.26 0.18, 19.48 0.31, 14.27

>80

Race

Mexican American

0.05, 42.48

Other Hispanic

0.08, 5.08

Non-Hispanic White Non-Hispanic Black

0.11, 11.00 0.08, 19.48 0.05, 25.27

Other

Gender

Male

1.161 0.869

0.036 0.021

0.05, 42.48 0.05, 14.26

Female

Poverty status In poverty

1.045 1.032 0.980

0.058 0.068 0.028

0.05, 17.02

Near poverty Not in poverty

0.07, 8.03

0.05, 25.27

Smoking history* Yes

1.320 0.942

0.044 0.027

0.12, 42.48 0.05, 25.27

*Lifetime history of smoking ≥100 cigarettes.

ing as Other Race, males, individuals living in poverty, and individuals with a lifetime history of smoking ≥100 cigarettes all had the highest BLLs in their respective demo- graphic groupings. In all, approximately 50% of people in the U.S. were found to have a BLL >0.76 μg/dl in 2017–2018. The percentage of individu- als in the >0.76 μg/dl BLL group varied by demographic characteristics such as race, age group, gender, poverty status, and 100-ciga- rette history. The dierences in distribution of BLL were significant for race ( p = .0003), age group ( p < .0001), gender ( p < .0001),

and 100-cigarette history ( p < .0001). These dierences, however, were not significant for poverty status group. These results are dis- played in Table 3. Logistic regression modeling was per- formed for individuals in or near poverty compared with individuals not in poverty (Table 4). Unadjusted logistic regression for the poverty status group with an outcome of BLL >0.76 μg/dl was not significant for either group. With the referent group set as individ- uals not in poverty, the unadjusted odds ratio for individuals near poverty was 1.158 (95% CI [0.903, 1.485], p = .2294) and for indi-

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this association was positive for people in the age group 35–44 years ( OR = 4.006, 95% CI [2.478, 6.477]) and the age group 45–54 years ( OR = 2.623, 95% CI [1.513, 4.546]). The three logistic regression models were used to calculate the change in total log-odds of having a BLL >0.76 μg/dl per one unit increase in poverty-income ratio (Table 6). For the first, unadjusted model, one unit increase in the poverty-income ratio resulted in an insignificant change in total log-odds of BLL >0.76 μg/dl ( p = .8480). Model 2 produced a significant estimate of change in total log-odds (estimate: -0.1748, p < .0001). Lastly, model 3 had similar, but less pronounced, results (esti- mate: -0.126, p = .0047). Finally, stratified analysis by age group was performed using the fully adjusted model to calculate the change in total log-odds of hav- ing a BLL >0.76 μg/dl per one unit increase in poverty-income ratio (Table 7). This mod- eling produced significant results only for individuals in the age group 35–44 years (estimate: -0.3826, p = .0002) and age group 45–54 years (estimate: -0.2953, p = .0002). Discussion The present study sought to examine the prev- alence of BLLs >0.76 μg/dl sample median level in the U.S. population and investigate demo- graphic factors associated with higher BLLs in 2017–2018. The results of this analysis showed that approximately 50% of people in the U.S. had BLLs >0.76 μg/dl in 2017–2018 (which is expected, given that 0.76 μg/dl represents the sample median), with significant dier- ences demonstrated when considering race, age group, gender, and 100-cigarette history. Specifically, non-Hispanic Blacks and Mexi- can Americans, individuals 60–79 years and ≥80 years, males, and individuals with a his- tory of smoking ≥100 cigarettes had a higher prevalence of elevated BLLs compared with their respective counterparts. The dierences in mean BLL within each demographic group revealed that in 2017–2018, people in the U.S. ≥80 years of age, individuals identifying as Other Race, males, individuals living in pov- erty, and individuals with a lifetime history of smoking ≥100 cigarettes had the highest BLLs in their respective demographic groupings. While unadjusted logistic regression model- ing did not reveal any significant association, both adjusted models 2 and 3 showed that individuals living in or near poverty had sig-

TABLE 3

Weighted Distribution of Blood Lead Level (BLL) Less and Greater Than 0.76 μg/dl by Demographic From the 2017–2018 National Health and Nutrition Examination Survey

Demographic

% BLL <0.76 μg/dl

% BLL >0.76 μg/dl

p -Value a

Total

50.26

49.74

Age (years)

<.0001

>18

85.81 79.54 64.37 59.14 50.11 23.56 19.99 14.04 60.74 66.89 50.24 53.93 43.31

14.19 20.46 35.63 40.86 49.89 76.44 80.01 85.96 39.26 33.11 49.76 46.07 56.69

18–24 25–34 35–44 45–54 55–64 65–80

>80

Race

.0003

Mexican American

Other Hispanic

Non-Hispanic White Non-Hispanic Black

Other

Gender

<.0001

Male

64.57 70.88

35.43 29.12

Female

Poverty status

.4228

In poverty

52.63 48.98 53.01

47.37 51.02 46.99

Near poverty Not in poverty

Smoking history b

<.0001

Yes

34.98 54.15

65.02 45.85

a Wald chi-square p -value. b Lifetime history of smoking ≥100 cigarettes.

viduals in poverty was 0.985 (95% CI [0.740, 1.311], p = .9120). The first adjusted logis- tic regression model resulted in significantly positive odds ratios for individuals near pov- erty ( OR = 1.926, 95% CI [1.622, 2.287], p < .0001) and for individuals in poverty ( OR = 1.973, 95% CI [1.497, 2.599], p < .0001). The second adjusted model produced similar significant results for individuals near pov- erty ( OR = 1.744, 95% CI [1.388, 2.190], p = .0001) and for individuals in poverty ( OR = 1.824, 95% CI [1.324, 2.513], p = .0012).

The fully adjusted logistic regression model was repeated with stratification for age group (Table 5). This stratification resulted in many insignificant age-specific associations between poverty status and BLL. In this modeling, sta- tistics and odds ratios for the age group <18 years were not reported due to missing or non- positive weights. Stratification by age group for individuals living near poverty resulted in positive odds ratios for people only in the age group 35–44 years ( OR = 3.216, 95% CI [1.830, 5.652]). For individuals in poverty,

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nificantly higher odds of having a BLL >0.76 μg/dl compared with individuals not in pov- erty, after controlling for various demographic variables. These odds ratios were noteworthily high, with individuals in and near poverty showing almost twice the odds of a BLL >0.76 μg/dl. Subsequent age stratification of this third logistic regression model revealed di er- ences in the association’s strength. That is, for individuals living near poverty, the association was strongest among those individuals in the age group 35–44 years. For individuals living in poverty, this association was strongest for both the age groups 35–44 years and 45–54 years. These odds ratios represent drastically increased odds of a BLL >0.76 μg/dl. Additionally, the change in total log-odds estimates revealed significant inverse associa- tions between the poverty-income ratio and log-odds of a BLL >0.76 ug/dL. The signifi- cant estimates for models 2 and 3 represent that as income increases, total log-odds of a BLL >0.76 μg/dl decreases. Age stratification also revealed that the relationship between this change in poverty-income ratio and log- odds of a higher BLL was significant only in the age groups 35–44 years and 45–54 years, which was true for age-stratified modeling of BLLs and categorical poverty status. This study represents an important analysis in a nationally representative sample of the demographic factors that play into an indi- vidual’s risk of higher BLLs. It is clear from the presented findings that individuals living near or in poverty do, indeed, have signifi- cantly increased odds of increased BLLs, after adjusting for various other factors. The con- sideration of race, age, gender, and lifetime cigarette use in these models further supple- ments the significance of these findings. These findings align with existing research on lead exposure, in that demographic factors con- tribute greatly to BLLs (Bernard & McGeehin, 2003; Morales et al., 2005; Yeter et al., 2020). These studies examine threshold lead levels of 5 μg/dl; however, their consideration of vari- ous demographic and socioeconomic factors reflect the findings of this present analysis. Relevance This study’s quantification of the odds of hav- ing BLLs above the sample median provides valuable insight to public and environmental health practitioners. While great strides have been made in equal e orts of lead remediation

TABLE 4

Logistic Regression Results for Blood Lead Levels Greater Than 0.76 μg/dl by Poverty Status From the 2017–2018 National Health and Nutrition Examination Survey

Status

Model 1

Model 2 a

Model 3 b

In poverty

Parameter ( SE )

-0.0151 (0.1340)

0.6794 (0.1293)

0.6009 (0.1504)

p -value

.9120

<.0001

.0012

OR [95% CI]

0.985 [0.740, 1.311]

1.973 [1.497, 2.599]

1.824 [1.324, 2.513]

Near poverty

Parameter ( SE )

0.1463 (0.1168)

0.6553 (0.0806)

0.5560 (0.1069)

p -value

.2294

<.0001

.0001

OR [95% CI]

1.158 [0.903, 1.485]

1.926 [1.622, 2.287]

1.744 [1.388, 2.190]

Not in poverty

Parameter ( SE )

ref. ref. ref.

p -value

OR [95% CI]

a Model 2 was adjusted for race, gender, and age. b Model 3 was additionally adjusted for history of smoking ≥100 cigarettes. Note. CI = confidence interval; ref. = reference value.

in the U.S., this analysis reveals there are still clear disparities. After adjustment for demo- graphic factors, individuals living in or near poverty remain the group with the greatest odds of existing in the above-the-median group for BLLs. The significant variations in prevalence of above-the-median BLLs across demographic groups reflect that individuals of lower socio- economic status and minority groups continue to be in need of focused interventions. Limitations This study is subject to limitations. As is ever- present with survey research, the reliance on self-reporting of demographic characteristics can result in under- or over-reporting. Also, NHANES is not administered to individuals who are institutionalized or incarcerated, which limits study generalizability. Further, these data are cross-sectional, which makes it impossible to draw conclusions based on temporality. Moreover, the use of an arbitrary BLL threshold of 0.76 μg/dl, while producing significant results, does not consider lower levels at which lead is known to negatively a ect one’s health. The exclusion of individuals from 6–18 years from the stratified modeling also lim-

its generalizability and, because younger people are at a greater risk of health issues from lead exposure (Cui et al., 2022; Hou et al, 2013), this study omits an important subset of the population. It is also possible that residual confounding is present due to nonconsideration of living arrangements that are surveyed in the NHANES data. Yeter et al. (2020) also report a significant associa- tion between health insurance coverage and BLL, which represents just one of the addi- tional adjustment variables to consider in future analyses. The use of the 100-cigarette history as a smoking classification might omit information that could better adjust the odds of having a BLL greater than the sample median. Strengths This study draws from NHANES data which, when analyzed with proper consideration of weights, strata, and clustering, is a nation- ally representative sample of noninstitution- alized, nonincarcerated people residing in the U.S. Given this foundation, this study is highly generalizable to people in the U.S. who are >18 years, excluding the previously mentioned groups. The consideration of vari-

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TABLE 5

TABLE 6

Logistic Regression Results for Blood Lead Levels Greater Than 0.76 μg/dl by Poverty Status and Stratified by Age From the 2017–2018 National Health and Nutrition Examination Survey

Change in Total Log-Odds of Blood Lead Levels Greater Than 0.76 μg/dl per One Unit Increase in Poverty Income Ratio From the 2017–2018 National Health and Nutrition Examination Survey

Demographic

Model 3 a

Model

Change Estimate ( SE )

p -Value

95% CI

-0.0068 (0.03501) -0.1748 (0.03337) -0.1260 (0.03803)

.8480

<.0001

2 a 3 b

In poverty <18 b

.0047

–

a Model 2 was adjusted for race, gender, and age. b Model 3 was additionally adjusted for a history of smoking ≥100 cigarettes.

18–24

1.770

[0.645, 4.858]

25–34

1.078

[0.619, 1.876]

35–44

4.006

[2.478, 6.477]

45–54

2.623

[1.513, 4.546]

TABLE 7

55–64

1.371

[0.616, 3.052]

Change in Total Log-Odds of Blood Lead Levels Greater Than 0.76 μg/dl per One Unit Increase in Poverty Income Ratio Stratified by Age From the 2017–2018 National Health and Nutrition Examination Survey

65–80

2.071

[0.822, 5.214]

>80

4.191

[0.843, 20.847]

Near poverty <18 b

–

18–24

2.319

[0.617, 8.712]

Age (years)

Model 3 a

25–34

1.473

[0.948, 2.289]

Change Estimate ( SE )

p -Value

<18 b

–

35–44

3.216

[1.830, 5.652]

18–24 25–34 35–44 45–54 55–64 65–80

-0.02549 (0.1490) 0.05012 (0.0457) -0.38260 (0.0763) -0.29530 (0.0593) -0.00808 (0.1071) -0.11710 (0.0797) -0.06785 (0.1263)

.8664 .2902 .0002 .0002 .9409 .1622 .5992

45–54

1.578

[0.847, 2.940]

55–64

1.348

[0.544, 3.340]

65–80

1.336

[0.615, 2.902]

>80

0.890

[0.319, 2.483]

Not in poverty

ref.

>80

a Model 3 was adjusted for race, gender, age, and history of smoking ≥100 cigarettes. b Results omitted due to missing or nonpositive weights. Note. CI = confidence interval; ref. = reference value.

a Model 3 was adjusted for race, gender, age, and history of smoking ≥100 cigarettes. b Results omitted due to missing or nonpositive weights.

ous demographic variables allows for more accurate estimates of the e ect of the poverty- income ratio on BLLs. Performance of change in log-odds estimate modeling also allows for contextualization of these odds ratios across the range of poverty-income ratio values. Conclusion In conclusion, the present study highlights the greater-than-expected prevalence of BLLs >0.76 μg/dl in specific demographic and eco- nomic groups in the U.S. population. This study’s findings suggest that individuals liv- ing in or near poverty, particularly those age

35–44 and 45–54 years, are at a greater risk of having above-the-median BLLs. These find- ings have important implications for policies that are aimed at reducing lead exposure in these groups. Additionally, these findings support that screening e orts for elevated BLLs should be increased in groups of individuals living in or near poverty, as well as other higher-odds groups identified in this analysis. Future research should focus on identifying and developing e ective interventions to prevent increased lead exposure in these dispropor- tionately a ected groups. Despite the limita-

tions of this study, including the self-report- ing of demographic characteristics and the exclusion of the age group <18 years from the stratified analyses, these results provide valuable insights into the demographic fac- tors that impact BLLs in the U.S. population. Overall, this study underscores the urgent need for continued e orts to monitor and reduce lead exposure in the U.S. Corresponding Author: Michael Ricciardi, Independent Researcher, 6 Berlin Lane, Towaco, NJ 07082. Email: michael.ricciardi1@gmail.com.

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