NEHA May 2024 Journal of Environmental Health

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

OR

95% CI

1

-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.

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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May 2024 • Journal of Environmental Health

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