High Risk Zip Codes in NV

Nevada’s Lead Exposure Risk Index

DISCLAIMER: The Lead Exposure Risk Index and its associated data are meant for broad planning purposes only. This tool was created to target outreach and education efforts in high risk zip codes, based on the relative risk of childhood lead exposure across all zip codes with households in NV. A residential zip code LERI score is not a substitute for a clinical risk assessment using a childhood lead risk questionnaire as nationwide datasets and risk factor weights used as inputs for the LERI may not be representative of local data. Please keep in mind that historical and current lead hazards in surrounding areas can also increase the risk of lead exposure to your patients, regardless of the LERI score of their residential zip code.

Geographic Areas of Priority

ZIP codes in deciles 1, 2, and 3 are considered low-risk.
ZIP codes in deciles 4, 5, 6, and 7 are considered medium-risk.
Lastly, ZIP codes in deciles 8, 9, and 10 are considered as areas at high-risk for lead exposure and are NvCLPPP’s areas of priority.

Decile Range LERI Range Ranking
1st-3rd 0.10-2.09 Low-risk
4th-7th 2.09-2.56 Medium-risk
8th-10th 2.56-2.95 High-risk

LERI by risk ranking for Nevada’s ZIP codes.

Figure 1 highlights high-risk ZIP codes for the state.

Figure 1: ZIP codes with the highest lead exposure risk by county.

While blood lead surveillance data was not incorporated at this time due to low screening rates, the NvCLPPP plans to improve the collection of epidemiologic data to be able to include race and ethnicity of BLL above the BLRV and other BLL data in future surveillance maps.

Methodology

The Nevada Lead Exposure Risk Index (LERI) was developed at the ZIP code level using the following methodology:

Initially, we identified the key variables to incorporate into the index. To inform this selection process, we referred to the work of Jacob and colleagues (Jacobs, et al., 2002), who conducted an extensive study estimating the prevalence of significant LBP hazards in housing units across the country.

We extracted household data by ZIP code pertaining to six critical characteristics from the American Community Survey, specifically focusing on:

  • Age of housing
  • Housing unit type
  • Occupant status
  • Household income
  • Race
  • Ethnicity

These variables were chosen based on their inclusion in Jacob et al.’s study, and we obtained the percentages of housing units with lead hazards from their research:

Characteristic Percent with lead hazards
Age of housing
  After 1980 3%
  1960-1979 8%
  1940-1959 43%
  Before 1940 68%
Housing unit type
  Single family 26%
  Multifamily 19%
Occupant status
  Owner occupied 23%
  Renter occupied 30%
Household income
  <$30,000/year 35%
  ≥$30,000/year 19%
Race
  White 25%
  African American 29%
  Other 23%
Ethnicity
  Hispanic/Latino 32%
  Non-Hispanic/Latino 24%

Percentage of homes with lead hazards by household characteristics.

We made slight adjustments to the first two age of housing categories, “after 1980” and “1960-1979“, to align them with the available categories in our household data. The subsequent step involved calculating , the weighted risk score for each household characteristic, using the following formulas:

  • Age of housing

R_{AH} = \frac{{HH}_{80 +} \times 0.03 + {HH}_{60 - 79} \times 0.08 + {HH}_{40 - 59} \times 0.43 + {HH}_{40 -} \times 0.68}{Total\ Households}

  • Housing unit type

R_{TP} = \frac{{HH}_{single} \times 0.26 + {HH}_{multi} \times 0.19}{Total\ Households}

  • Occupant status

R_{OS} = \frac{{HH}_{owner} \times 0.23 + {HH}_{renter} \times 0.30}{Total\ Households}

  • Household income

R_{HI} = \frac{{HH}_{greater_than_30k} \times 0.35 + {HH}_{at_or_under_30k} \times 0.19}{Total\ Households}

  • Race

R_{RA} = \frac{{HH}_{White} \times 0.25 + {HH}_{AA} \times 0.29 + {HH}_{Other} \times 0.23}{Total\ Households}

  • Ethnicity

R_{ET} = \frac{{HH}_{hisp} \times 0.32 + {HH}_{nonhisp} \times 0.24}{Total\ Households}

, where {HH}_i indicates the number of households of a specific household characteristic.

The third step involved formulating the LERI, recognizing that it constitutes a weighted summation of the aforementioned weighted risk scores. Furthermore, we incorporated the percentage of children ages 6 and under years (Child6) and the poverty rate (Poverty) into the LERI formula.

As a result, the LERI formula appears as follows:

LERI = w_{AH} \times R_{AH} + w_{TP} \times R_{TP} + w_{OS} \times R_{OS} + w_{HI} \times R_{HI} + w_{RA} \times R_{RA} + w_{ET} \times R_{ET} + w_{Child6} \times Child6 + w_{Poverty} \times Poverty

, where w_{i} indicates the risk weight for a given risk score R_{i}.
Nonetheless, because of the lacking theoretical support to estimate the eight weights ((w_{AH},\ w_{TP},\ w_{OS},\ w_{HI},\ w_{RA},\ w_{ET},\ w_{Child6},\ w_{Poverty}<code>), the fourth step applied factor analysis to reconstruct the formula using a reduced number of factors that could still adequately represent the original eight variables. The updated formula is expressed as:

LERI = \sum_{i = 1}^{p}{w_{p} \times FS}_{p} + c

where p is less than eight, w_{p} is the weight of the pth factor score {FS}_{p}, and c is a constant used to scale the LERI, ensuring that it remains strictly positive.

We identified the first four factors, accounting for 31.49%, 21.44%, 13.34%, and 12.18% of variance explained. This collective explanation of 78.44%of the variance from the original eight variables demonstrates the effectiveness of these factors in capturing the underlying patterns.

The fifth step was to compute the weights of the four selected factors. We rescaled their proportions of variance {Prop}_{x} explained by w_{j} = \frac{{Prop}<em>{j}}{\sum</em>{i = 1}^{2}{Prop}_{i}} to make the total weights equal to 1.

The four weights were computed as:

w_{j} = \frac{{Prop}_{j}}{\sum_{i = 1}^{2}{Prop}_{i}}
w_{1} = \frac{0.3149}{0.3149 + 0.2144 + 0.1334 + 0.1218} = 0.40
w_{2} = \frac{0.2144}{0.3149 + 0.2144 + 0.1334 + 0.1218} = 0.27
w_{3} = \frac{0.1334}{0.3149 + 0.2144 + 0.1334 + 0.1218} = 0.17
w_{4} = \frac{0.1218}{0.3149 + 0.2144 + 0.1334 + 0.1218} = 0.16

Therefore, the final index score can be formulated as:

LERI = 0.40 \times {FS}_{1} + 0.27 \times {FS}_{2} + 0.17 \times {FS}_{3} + 0.16 \times {FS}_{4} + 1.12

The factor analysis was exclusively applied to ZIP codes with households. ZIP codes without households are postal routes that correspond to address groups or delivery routes and are not a representation of physical boundaries, buildings, or populations. Consequently, a total of 15 ZIP codes without households were excluded from the analysis.

Lastly, we categorized the calculated LERIs into deciles, with each decile representing 10% of the remaining 175 ZIP codes in Nevada:

Decile LERI
1st 0.10-1.33
2nd 1.33-1.75
3rd 1.75-2.09
4th 2.09-2.31
5th 2.31-2.46
6th 2.46-2.52
7th 2.52-2.56
8th 2.56-2.62
9th 2.62-2.68
10th 2.68-2.95

Lead exposure risk index range by decile for Nevada’s ZIP codes.