His research — included in his book — found that children born in places and times when the air pollution was especially bad grew up to be not as tall as children born in less-polluted times in the same locality. In India, long-term exposure to outdoor and household air pollution contributed to over 1. Globally, air pollution is estimated to have contributed to 6. It was the fourth leading risk factor for early death worldwide in , surpassed only by high blood pressure, tobacco use and poor diet, found the report.
While several countries, especially India and China, had shown progress in terms of reducing household air pollution by making cleaner cooking fuels available over the last decade between , their actions to tackle outdoor or ambient air pollution remained stagnant during the same period. Overall outdoor air pollution levels in India have increased in the last decade. In , Indians were exposed to the most air pollution ever calculated from the population-weighted annual outdoor air pollution concentration, as we explain later , the report said.
This report comes at a time when Covid has claimed more than , lives in India. And evidence suggests that people with heart and lung conditions are vulnerable to a more severe form of Covid Furthermore, India is moving into the winter, which exacerbates air pollution in several Indian cities already infamous for high levels of air pollution and smog, especially in the north.
Since evidence has clearly linked air pollution and increased risk of heart and lung diseases, there was growing concern that exposure to high levels of air pollution during winter months could exacerbate the effects of Covid, the Health Effects Institute statement said. In , air pollution contributed to nearly , infant deaths worldwide.
In India, a fifth of neonatal deaths from all causes can be attributed to air pollution. The first month is already a vulnerable time for newborn babies. This data on PM2. The clusters of households which are sampled in this survey are geo-coded, which allows us to construct local 1 measures of exposure to pollution for children who belong to these clusters.
We use the cluster location, date of birth, and pregnancy duration for each child to create trimester level 3-month periods mean pollution exposure for each child in our sample. We plot the relationship between child growth indicators and age in Figure 2 for two groups of children — one with low level of exposure to pollution 1st Quintile versus those who had high level of exposure 5th Quintile to pollution during their first trimester.
We find that children who are exposed to high levels of pollution have worse child health outcomes as shown by the solid plot that lies below the dashed plot for children who were exposed to lower levels of pollution. Note : Polynomial fit plot between height-for-age and child's age in months for children who had low level of exposure to pollution 1st Quintile versus those who had high level of exposure 5th Quintile to pollution during their first trimester.
Our estimation sample comprises of close to , children for whom complete pollution exposure history was available for the in-utero period. We use an instrumental variable IV strategy 3 to identify the effect of air pollution on child health.
Using wind direction, we are able to identify upwind fire events 4 exogenous or external variable in neighbouring areas, which we use as an instrument for local pollution levels endogenous or variable determined internally in the model 5. We also control other demographic characteristics of the household, mother, and child. Our analysis shows that fire events affect pollution positively the first stage of an IV regression.
One standard deviation 6 change in upwind fire-events is associated with an increase in PM2. Our main results show that exposure to air pollution during the first trimester has a negative effect on child growth indicators.
A standard deviation unit change in mean PM2. We also conduct various robustness tests to establish the validity of our results. Stunting affects GDP gross domestic product of a nation via three channels: lower returns to lower education, lower returns to lower height, and lower returns to lower cognition.
We use an estimate of probability of being stunted due to exposure to outdoor pollution, and find that one standard deviation increase in outdoor pollution leads to a 0. We take into account the variation in our data to further assess if a section of the population is more likely to suffer from the negative effects of pollution. Each result shown is from a separate fixed effects regression of child height-for-age on the average exposure to PM 2.
The three tests for non-linear concentration-response functions each failed to reject that a linear shape best fits the data. Moreover, each approach suggests that, if anything, effects may be steeper at higher concentration levels. Specifically, column 7 of Table 2 shows that a natural log functional form — consistent with a concentration-response function exhibiting diminishing marginal costs — fits the data less well than the linear form.
Column 8 includes a linear spline that allows a different slope above the median level of ambient PM 2. Although this model does not fit the data better than a simple linear form, the negative sign on the coefficient suggests the possibility of a steeper concentration-response function at higher levels of exposure.
Additional file 1 : Figure S1 demonstrates that none of the polynomial forms we tested quadratic through quantic improve on a linear functional form, while Additional file 1 : Figure S2 — the Box-Cox transformation — indicates that a model with slightly increasing marginal effects may best fit the data. In Additional file 1 : Table S2, we present results from statistical analyses similar to columns 6 through 8 of the the main Table 2 , the difference is that models presented in the supplementary table include all coverates, including birth, mother, and household characteristics, rather than birth characteristics only.
The inclusion of these additional control variables does not change the interpretation of these analyses. We also show that the model is robust to replacing district-month fixed effects with PSU-month fixed effects, a finer measure of seasonality. We report the first evidence of an association between ambient PM 2. We find that an increase in PM 2.
Consistent with evidence in the literature that shocks in utero and early-life are critical for child development outcomes [ 40 , 46 ], we find evidence that exposure to PM 2. The average child in our data is exposed to a PM 2. Using the estimates from our analysis, this means that the average child is about 0. Although this effect is small relative to other environmental factors affecting child health, such as open defecation [ 11 ], it influences all of the almost 30 million births per year that occur in India.
Moreover, the difference between the children in our sample most exposed to PM 2. Therefore, based on our findings, the most exposed children in India are about 0. This projected difference — 0. Since child growth is highly correlated with early-life mortality [ 47 ], the associations we observed in this study are suggestive of an association between PM 2.
In the data we use for this analysis, a district where children are 0. Our study does not allow us to observe disease directly; however, mechanisms in the literature are consistent with the association that we document. For example, exposure to particulate matter is associated with lower birth weight [ 6 , 7 ], which is in turn linked to stature in childhood [ 48 ]. Similarly, exposure to ambient air pollution is associated with the incidence of pneumonia [ 49 , 50 ].
Respiratory infections, like pneumonia, sometimes occur with fevers which can suppress the appetite, and reduce nutrient intake [ 51 ]. Moreover, infection and inflammation are metabolically demanding and may reallocate resources at the expense of growth [ 52 ]. This study has several limitations. One is the possibility of residual confounding. For example, we were not able to control for potential co-pollutants such as ozone or NO 2 , for which data is not available.
However, we have no reason to believe that these practices would have changed for a large proportion of households. Similarly, we assumed that surveyed mothers delivered their children in the same district in which they were surveyed. Finally, we rely on district-level measures of exposure derived from satellite data, thus raising the possibility of measurement error. However, assuming this error is random, the consequence would be attenuation towards the null, meaning that the true size of the effect of PM 2.
In light of these limitations, we encourage additional research on this topic. If possible, this would include other study designs cohort studies, natural experiments etc. To our knowledge, this is the first study to directly estimate the impact of early-life exposure to ambient PM 2. Ambient PM 2. Therefore, the health burden that we quantify here could potentially increase unless appropriate policy action is taken to reduce air pollution throughout India.
In particular, although policy conversations often focus on Delhi and, to a lesser extent, other big cities , we find effects throughout India, and on both rural and urban children, suggesting that the policy challenges are broader than is commonly understood. Because child height has lasting consequences for human capital [ 10 , 40 ], this is a problem with potential ramifications throughout the Indian society and economy.
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