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]
No
*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
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May 2024 • our6*l o/ 6=2ro6me6;*l e*l;1
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