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Estimating critical level of \(\hbox {PM}_{{10}}\) to affect hospital infant admissions in Vitória, Brazil

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Abstract

The deleterious effects of air pollution on children’s health are broadly discussed in the literature. Among pollutants, the Particulate Matter with diameter \(<{10}\,\upmu \hbox {m}\) (\(\hbox {PM}_{10}\)) is usually associated with an increase in the number of children’s hospital admissions. In this paper, we have assumed that, at low concentrations, there is no effect of the former on the latter, and from an unknown concentration threshold, the admissions would be positively affected by \(\hbox {PM}_{10}\). As far as we know, this scenario has never been explored before. In this study, the number of children (10 years and younger) daily admitted with respiratory diseases, the daily average concentration of the \(\hbox {PM}_{10}\) pollutant and meteorological variables collected in the period of January 2010–August 2014, and a deterministic factor for workdays and days off are considered in the model. In order to deal with the lack of information regarding the residence of the children, regularized canonical correlation analysis was used to select only admissions from address codes with higher correlations with \(\hbox {PM}_{10}\) collected from the considered monitoring station. To explain the assumed behavior and estimate the \(\hbox {PM}_{10}\) threshold, we used segmented regression analysis for count data. The Negative Binomial segmented model provided a better fit. Despite data limitations, this framework proves to be a promising modeling strategy, since it is able to provide an explicit estimate for a threshold of \(\hbox {PM}_{10}\) exposure. Another important finding is that the inadequate adjustment by non-segmented models would underestimate the number of expected hospital admissions for concentrations far from the threshold, which in a cumulative aspect may lead to an inaccurate hospital management. We also developed a Monte Carlo experiment to investigate the effects of misfitting the segmented behavior by classic count models, which ratified the findings in application.

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Acknowledgements

The authors would like to thank the Nossa Senhora da Glória Children’s Hospital for providing the data related to the number of hospitalizations and to IEMA for making freely available air pollution and meteorological variables.

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Authors and Affiliations

  1. LECON, Statistics Department, Federal University of Espírito Santo, Vitória, Brazil

    Alessandro J. Q. Sarnaglia, Luciana G. Godoi & Mariana C. Rodrigues

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  1. Alessandro J. Q. Sarnaglia
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  2. Luciana G. Godoi
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  3. Mariana C. Rodrigues
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Correspondence to Luciana G. Godoi.

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Appendix

Appendix

In this section, we present the complementary results of the simulation experiment. This information regards to the Mean and the RMSE of the estimates (Tables 9 and 10) under the NB scenario, and the frequency of inadmissible \(\psi\) values (Table 11).

Table 9 Mean of coefficient estimates under the NB response, with \(\theta =0.5\)
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Table 10 RMSE of the estimated regression coefficients under the true (segmented) Negative Binomial scenario, with \(\theta =0.5\)
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Table 11 Frequency of inadmissible \(\psi\) values for NB response
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Sarnaglia, A.J.Q., Godoi, L.G. & Rodrigues, M.C. Estimating critical level of \(\hbox {PM}_{{10}}\) to affect hospital infant admissions in Vitória, Brazil. Stoch Environ Res Risk Assess 35, 2031–2048 (2021). https://doi.org/10.1007/s00477-021-01979-1

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  • DOI: https://doi.org/10.1007/s00477-021-01979-1

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