Dataset Information

Assessing the population-wide exposure to lead pollution in Kabwe, Zambia: an econometric estimation based on survey data.

ABSTRACT: This study quantitatively assessed the population-wide lead poisoning conditions in Kabwe, Zambia, a town with severe lead pollution. While existing data have reported concerning blood lead levels (BLLs) of residents in pollution hotspots, the data representing the entire population are lacking. Further, selection bias is a concern. Given the lack of compulsory testing schemes, BLLs have been observed from voluntary participants in blood sampling surveys, but such data can represent higher or lower BLLs than the population average because of factors simultaneously affecting participation and BLLs. To illustrate the lead poisoning conditions of the population, we expanded the focus of our surveys and then econometrically estimated the BLLs of individuals representing the population, including those not participating in blood sampling, using background geographic, demographic, and socioeconomic information. The estimated population mean BLL was 11.9 ?g/dL (11.6-12.1, 95% CI), lower than existing data because of our wide focus and correction of selection bias. However, the scale of lead poisoning remained immense and 74.9% of residents had BLLs greater than 5 ?g/dL, the standard reference level for lead poisoning. Our estimates provide a deeper understanding of the problem and a foundation for policy intervention designs.

SUBMITTER: Yamada D

PROVIDER: S-EPMC7492281 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Assessing the population-wide exposure to lead pollution in Kabwe, Zambia: an econometric estimation based on survey data.

Yamada Daichi D Hiwatari Masato M Hangoma Peter P Narita Daiju D Mphuka Chrispin C Chitah Bona B Yabe John J Nakayama Shouta M M SMM Nakata Hokuto H Choongo Kennedy K Ishizuka Mayumi M

Scientific reports 20200915 1

This study quantitatively assessed the population-wide lead poisoning conditions in Kabwe, Zambia, a town with severe lead pollution. While existing data have reported concerning blood lead levels (BLLs) of residents in pollution hotspots, the data representing the entire population are lacking. Further, selection bias is a concern. Given the lack of compulsory testing schemes, BLLs have been observed from voluntary participants in blood sampling surveys, but such data can represent higher or lo ...[more]

PMID: 32934309

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Project description:BackgroundThe era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.MethodsWe introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM2.5 predictions at 17 monitors in 8 US cities.ResultsIn simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R2 = 0.95) with health association bias compared to overall approaches (R2 = 0.57). For PM2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.ConclusionsFor epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Dataset Information

Assessing the population-wide exposure to lead pollution in Kabwe, Zambia: an econometric estimation based on survey data.

Publications

Assessing the population-wide exposure to lead pollution in Kabwe, Zambia: an econometric estimation based on survey data.

Similar Datasets

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