Measuring Burden of Disease Attributable to Air Pollution Due to Preterm Birth Complications and Infant Death in Paris Using Disability-Adjusted Life Years (DALYs)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Health Databases and Outcomes Definition
2.3. Ethical Approval
2.4. Census Block Neighborhood Socioeconomic Level
2.5. Air Pollution Measurement
2.6. Statistical Methods
2.6.1. Estimation of Number of Attributable Health Events
- (i)
- Population size (in this study, the number of live births per year among residents of Paris) and level of exposure (population exposure),
- (ii)
- baseline of infant death rate and preterm birth rate,
- (iii)
- dose–response function derived from meta-analysis, which estimated the meta-relative risk, associated with the increase of PM10 exposure [20]. In our study, number of health events that could be attributed to PM10 exposure were assessed for hypothetical air pollution reductions in line with WHO recommendations (for PM10, it was 20 μg/m3 called the counterfactual value), using the following Equation (1).
- Y0 corresponds to the total number of observed infant deaths or preterm births,
- Δx measures the difference between the observed average of the PM10 and the guideline value of WHO, and,
- β is the natural logarithm of meta relative risk expressed for an increase of 10 µg/m3 of PM10
2.6.2. Calculation of Years of Life Lost (YLL) Attributed to Infant Mortality
- N is the number of deaths
- L is the standard life expectancy in years
2.6.3. Calculation of Disability-Adjusted Life Years (DALYs) Attributed to Preterm Birth
- YLD = P × DW
- P is the number of prevalent cases, and
- DW is the disability weight
2.6.4. Disability Weights
3. Results
3.1. Baseline Health Data and PM10 Data
3.2. DALYs for Preterm Birth Complications
3.3. Burden of Disease Attributed to PM10 Exposure
3.4. Unequal Social Distribution
4. Discussion
- (i)
- The generalization of this evidence. Many recent studies, including meta-analysis [7] have concluded that air pollution may have adverse pregnancy outcomes such as preterm birth, small head circumference at birth, as well as neonatal and post-neonatal mortality. For instance, Pedersen et al. found significant association between PM10 and NO2 exposure during pregnancy and the risk of low birthweight at term PM10 (OR for 10 μg/m3 increase 1·16, 95% CI = 1.00–1·35), NO2 (OR for 10 μg/m3 increase 1·09, 1·00–1·19) [30]. In European country, Schifano et al., found consistently in Rome and Barcelona, an increased risk of preterm birth (in a more pronounced way among the early preterm birth) for a unit increase in PM10 and NO2 [33]. In addition, a large population-based prospective cohort study conducted in Rotterdam found that maternal exposure to PM10 and NO2 is inversely associated with fetal growth and with weight at birth. The authors revealed that only PM10 exposure levels were positively associated with preterm birth and Small Gestational Age (SGA) [34]). Even scientific evidences tend to reveal that the risk of adverse birth outcomes increase with the increase in air pollution level during pregnancy, several studies did not confirm this finding. For instance, European meta-analysis of 13 cohort studies did not found association between risk of preterm birth and atmospheric pollutants [35]). However, as the authors discussed, they did not take into account information on maternal, fetal and placental conditions to identify preterm birth cases with different proximal etiology.
- (ii)
- Evidences for causality. It is also important to recall the existence of uncertainty in any epidemiological studies (detailed above) suggesting (or not) significant association between air pollution exposure and birth outcomes. This uncertainty may affect the causal relationships or the shape of the exposure–response relation. Thus, due to these uncertainties may bias the input data of the Environmental Burden of Disease (EBD) and turn in uncertain assessment of DALYs, which have to be interpreted with caution.
- -
- The combinations of multiple sources of input data that have each their own level of uncertainty.
- -
- The assumptions choice in the applicability of the exposure or exposure–risk relationship to the country of concern.
- -
- The multiple steps structuring the method, probably tend to accumulate the uncertainty.
- -
- The few number of available studies assessing the risk of neurodevelopmental impairment among the preterm population, crucial information to estimate the DALYS.
- -
- Potential biases in information include, for instance, the exposure–risk relationship derived from epidemiological studies or meta-analysis studies.
- -
- Heterogeneity or validity of data sources for instance, the measure of exposure and health data.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Gestational Age | 20–28 Weeks | 28–32 Weeks | 32–36 Weeks |
---|---|---|---|
Risks Estimates | % 95% CI | % 95% CI | % 95% CI |
Case fatality risks (CFR) | 28.3% (95% CI = 25.4–31.2%) | 5.8% (95% CI = 5.1–6.5%) | 0.7% (95% CI = 0.6–0.8%) |
Risk of moderate/severe long-term neurodevelopmental Impairment | 24.5% (95% CI = 20.2–28.8%) | 12.2% (95% CI = 6.1–18.2%) | 1.8% (95% CI = 1.5–2.3%) |
Risk of mild long-term neurodevelopmental Impairment | 33.9% (95% CI = 28.6–39.3%) | 16.5% (95% CI = 6.1–18.2%) | 3.4% (95%CI = 2.5–4.4%) |
Input Data/Parameters | Data Source/Reference | Descriptive |
---|---|---|
Concentration of PM10 between 2007 and 2012 | The local association (AirParif)—using ESMERALDA Atmospheric Modeling system [18] | Mean 30.1 µg/m3 (Standard Deviation = 1.72 μg/m3) |
Population size (the number of live births) | The birth certificate information registered by the Maternal and Child Care department of Paris | Total number of births = 86,877 |
Infant death over the period 2004–2009 | Death certificates recorded at Paris City Hall | 3.36 per 1000 live births |
Preterm birth between 2009 and 2011 | The birth certificate information registered by the Maternal and Child Care department of Paris | The rate of preterm birth reached 6.1% |
Age group | ||
Preterm births among all births at less than 28 weeks and >20 weeks. | The birth certificate information registered by the Maternal and Child Care department of Paris | Rate of Preterm births equal to 0.22% |
Preterm births among all births at 28–31 weeks | The birth certificate information registered by the Maternal and Child Care department of Paris. | Rate of Preterm births equal to 0.62% |
Preterm births among all births at 32–36 weeks | The birth certificate information registered by the Maternal and Child Care department of Paris | Rate of Preterm births equal to 5.34% |
Dose–response function: Derived from meta-analysis | ||
For Infant death | Rojas-Rueda et al. [22] | Risk of death among the infant population is 1.04 (95% CI = 1.02–1.07) for an increase of 10 µg/m3 in PM10 concentrations. |
For preterm birth | Guo, et al. 2019 [21] | Increased risk of preterm birth related to an increase of 20 µg/m3 of PM10 concentrations is equal to 1.05 (95% CI = 1.02–1.07) |
Disability weights | Blencowe, et al. 2013 [25] | For moderate-to-severe neurodevelopmental impairment: 0.38 (uncertainty range: 0.29–0.49). Assuming that about 50% of those with mild impairment had isolated mild problems, and 50% had mild motor and mild cognitive impairment, the disability weight used for these people was 10 fold lower, at 0.03 (uncertainty range: 0.02–0.05) |
Neighborhood Socioeconomic Level | N | Death Rate per 1000 Live Births |
---|---|---|
1 (less deprived) | 70 | 2.4 |
2 | 105 | 3.1 |
3 | 107 | 3.0 |
4 | 136 | 3.2 |
5 (most deprived) | 205 | 4.5 |
TOTAL | 623 & | 3.3 |
Ratio § (1/5) | 1.9 |
Neighborhood Socioeconomic Level | Extremely Preterm (<28 Weeks *) | Moderate Preterm (28–32 Weeks) | Late Preterm (32–36 Weeks) | TOTAL | ||||
---|---|---|---|---|---|---|---|---|
N | % | N | % | N | % | N | % | |
1 (less deprived) | 27 | 14.9 | 90 | 18.3 | 874 | 19.8 | 991 | 19.5 |
2 | 37 | 20.4 | 102 | 20.7 | 828 | 18.8 | 967 | 19.0 |
3 | 35 | 19.3 | 106 | 21.5 | 826 | 18.7 | 967 | 19.0 |
4 | 30 | 16.6 | 68 | 13.8 | 915 | 20.7 | 1013 | 19.9 |
5 (most deprived) | 52 | 28.7 | 127 | 25.8 | 967 | 21.9 | 1146 | 22.5 |
TOTAL | 181 | 100% | 493 | 100% | 4410 | 100% | 5084 & | 100% |
Ratio § (1/5) | 1.92 | 1.41 | 1.11 | 1.16 |
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Deguen, S.; Marchetta, G.P.; Kihal-Talantikite, W. Measuring Burden of Disease Attributable to Air Pollution Due to Preterm Birth Complications and Infant Death in Paris Using Disability-Adjusted Life Years (DALYs). Int. J. Environ. Res. Public Health 2020, 17, 7841. https://doi.org/10.3390/ijerph17217841
Deguen S, Marchetta GP, Kihal-Talantikite W. Measuring Burden of Disease Attributable to Air Pollution Due to Preterm Birth Complications and Infant Death in Paris Using Disability-Adjusted Life Years (DALYs). International Journal of Environmental Research and Public Health. 2020; 17(21):7841. https://doi.org/10.3390/ijerph17217841
Chicago/Turabian StyleDeguen, Séverine, Guadalupe Perez Marchetta, and Wahida Kihal-Talantikite. 2020. "Measuring Burden of Disease Attributable to Air Pollution Due to Preterm Birth Complications and Infant Death in Paris Using Disability-Adjusted Life Years (DALYs)" International Journal of Environmental Research and Public Health 17, no. 21: 7841. https://doi.org/10.3390/ijerph17217841
APA StyleDeguen, S., Marchetta, G. P., & Kihal-Talantikite, W. (2020). Measuring Burden of Disease Attributable to Air Pollution Due to Preterm Birth Complications and Infant Death in Paris Using Disability-Adjusted Life Years (DALYs). International Journal of Environmental Research and Public Health, 17(21), 7841. https://doi.org/10.3390/ijerph17217841