NEHA October 2022 Journal of Environmental Health

14 Volume 85 • Number 3 used in the state, and thus lead to modifications to improve the process. Some studies have observed this inadequacy in the screening process of BLL surveillance data. Based on estimates of elevated BLL (≥10 µg/dL) data for children 1–5 years from 1999–2010 for 39 states (including Washington, DC) that were reported to CDC, Roberts et al. (2017) found that approximately 1.2 million children had elevated BLLs. Among these, 337,405 (approximately 28%) were not reported because of incomplete case ascertainment and far fewer cases were ascertained in the South and West regions. In Georgia, the case ascertainment ratio (i.e., the number reported/number of cases) was only 0.10. This finding points to undertesting of children with elevated BLL in many states, including Georgia. Similar results have been observed from other studies. According to data from the California Department of Health Care Services during 2009–2010 through 2017–2018, fewer than 27% of eligible children in California received all the required blood tests they should have, although many of these children lived in areas of the state with occurrences of elevated BLLs (Auditor of the State of California, 2020). Although these studies point to the inadequacy of the screening process for children, no study showed how inadequacy can a•ect actual BLLs among children <6 years. Our study fills the gap in that research and detects the discrepancy between estimated and observed numbers of children with higher (i.e., 5–9 µg/dL) BLLs—a discrepancy that resulted, most likely, from an undertesting of children with elevated BLLs. Most importantly, we find the corrected number of children with higher (i.e., 5–9 µg/dL) BLLs. Limitations Our study is subject to several limitations. For example, we assumed that the neighboring counties have similar BLL rates to what was found in the targeted county, which might not be true. If the neighboring counties do not have similar BLL rates, then the prior and posterior distributions of the parameter θ in the targeted county (Equation 9) will be distorted. The equation might still provide a reasonably reliable estimate, however, of the number of children with BLLs of 5–9 µg/dL in the targeted county, which is possible because prior p(θ) and posterior p(θ/z) occur in the numerator and denominator, respectively, of Equation 9 and might, to some extent, nullify each other’s distorting e•ect. If the risk factors for elevated BLLs in the targeted county, however, vastly di•er from those in the neighboring counties, then this approach might not give a good estimate. We also assumed that the number of children with BLLs of 5–9 µg/dL followed a Poisson distribution and the BLL rate was distributed as gamma. The results might change if these model assumptions were modified. Conclusion We observed underreporting of children <6 years with BLLs of 5–9 µg/dL in some counties of Georgia. This finding is based on the application of a Bayesian model on county data. More research is needed to investigate BLLs among children to ensure they are adequately protected from lead poisoning. Our study has the appeal of being applied in any situation where surveillance data are collected to obtain vital information in institutions or communities, such as hospital-acquired infection in a specific hospital. For example, assuming that the rate of infection is similar to other hospitals in the vicinity, one can check the validity of the rates in this specific hospital and possibly correct it, if necessary, as we did in our study. Similar situations can arise in estimating heart transplant mortality in a hospital, or, as another example, estimating crime rate in a community from self-reported statistics. Our study, then, highlights a general approach to verify useful information and details an opportunity to estimate an actual value or index from observed data. Disclosure: The findings and conclusions in this article are those of the author and do not necessarily represent the o¤cial position of CDC. A D VANC EME N T O F T H E SCIENCE Plot for the Predictive Density of All Children <6 Years With Blood Lead Levels (BLLs) of 5–9 μg/dL in the Targeted Counties in the West and Central Regions of Georgia, 2015 Note. The observed value of children with BLLs of 5–9 μg/dL in the targeted county in the West region was 14 among 1,587 children tested. The observed value of children with BLLs of 5–9 μg/dL in the targeted county of the Central region was 1 among 170 children tested. 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 # of Children <6 Years With BLLs of 5–9 µg/dL 0 0.05 0.10 0.15 0.20 0.25 0.30 Probability Probability in the Central Region Probability in the West Region FIGURE 1