Exposure to landscape fire smoke reduced birthweight in low- and middle-income countries: findings from a siblings-matched case-control study

  1. Jiajianghui Li
  2. Tianjia Guan
  3. Qian Guo
  4. Guannan Geng
  5. Huiyu Wang
  6. Fuyu Guo
  7. Jiwei Li
  8. Tao Xue  Is a corresponding author
  1. Institute of Reproductive and Child Health / Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, China
  2. Department of Health Policy, School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College, China
  3. School of Energy and Environmental Engineering, University of Science and Technology, China
  4. School of Environment, Tsinghua University, China
  5. College of Computer Science and Technology, Zhejiang University, China
4 figures, 1 table and 4 additional files

Figures

Distribution of the fire-sourced PM2.5 concentrations (average for 2000–2014) and the locations of the analyzed livebirths (gray dots).

The circles represent the rate of very low birthweight births among the analyzed livebirths by country.

Figure 2 with 3 supplements
Effects of fire-sourced PM2.5, estimated by linear models.

The dots with error bars show the estimated associations between gestational exposure to fire-sourced PM2.5 and birthweight change, low birthweight, or very low birthweight. The dots represent the point estimates, and bars represent the corresponding 95% confidence intervals.

Figure 2—figure supplement 1
The estimated associations between fire-sourced PM2.5 and birthweight change, low birthweight or very low birthweight, by different lags.

The nth lagged exposure is defined as the concentration of fire-sourced PM2.5 during the nth month before birth. The results are estimated from the lag-distributed models.

Figure 2—figure supplement 2
The cumulated birthweight change associated with an average of fire-sourced PM2.5 concentrations from a lagged month to birth.
Figure 2—figure supplement 3
The subpopulation-specific associations between gestational exposure to fire-sourced PM2.5 and birthweight change, low birthweight and very low birthweight.
The nonlinear association between gestational exposure to fire-sourced PM2.5 and change in birthweight.

The solid line represents the point estimates, the dashed line represents the 95% confidence intervals, the boxplots represent the distributions of different exposure levels by regions, and the red dots represent the mean exposure level.

Figure 4 with 1 supplement
Baseline-varying effect of fire-sourced PM2.5 on birthweight.

The lines show the estimated association (y-axis) between gestational exposure to fire-sourced PM2.5 and relative change in birthweight, given different baseline birthweights (x-axis). The solid line represents the point estimates, the dashed line represents the 95% confidence intervals, and the boxplots represent the distributions of different birthweights by region.

Figure 4—figure supplement 1
The baseline-varying association between gestational exposure to fire-sourced PM2.5 and absolute birthweight change.

The solid line presents the point estimates, the dashed line presents the 95% confidence intervals, and the boxplots present the distributions of different birthweight levels by regions.

Tables

Table 1
Between- or within-group variations in birthweight and gestational exposure to fire-sourced PM2.5 and their correlations.
Standard deviation (% of total variance)Correlation (R)
Birthweight(g)Fire-sourced PM2.5 g/m3
Total724 (100%)5.53 (100%)0.1594
Between groups of matched siblings613 (71.7%)5.30 (91.7%)0.1973
Within groups of matched siblings386 (28.3%)1.59 (8.3%)−0.0035

Additional files

Supplementary file 1

Summary of analyzed variables.

(a) Comparison between the PM2.5 concentrations simulated by GEOS-Chem model and those estimated from satellite measurements and other inputs. In the comparison, the satellite-based estimates are utilized as the gold-standard referent values and the GEOS-Chem simulations as predictions. (b) Population characteristics.

https://cdn.elifesciences.org/articles/69298/elife-69298-supp1-v2.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/69298/elife-69298-transrepform1-v2.docx
Source code 1

The R codes and data to reproduce Figures 14.

https://cdn.elifesciences.org/articles/69298/elife-69298-supp2-v2.zip
Reporting standard 1

Checklist of STROBE items.

https://cdn.elifesciences.org/articles/69298/elife-69298-repstand1-v2.doc

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A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

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  1. Jiajianghui Li
  2. Tianjia Guan
  3. Qian Guo
  4. Guannan Geng
  5. Huiyu Wang
  6. Fuyu Guo
  7. Jiwei Li
  8. Tao Xue
(2021)
Exposure to landscape fire smoke reduced birthweight in low- and middle-income countries: findings from a siblings-matched case-control study
eLife 10:e69298.
https://doi.org/10.7554/eLife.69298