Determining the effect of air quality on activities of daily living disability: using tracking survey data from 122 cities in China | BMC Public Health

Jennifer E. Engen

Benchmark regression

In the benchmark regression, the effects of different pollutant concentrations were tested, and the results are presented in Table 3. Models (1)–(3) are the results of the stepwise test of air pollutant concentration effects, controlled by individual and time effects, whereas Model (4) is based on the AQI. The results show that both SO2 and PM10 have significant and negative effects on ADL disability. The significance level of SO2 was low, whereas the results for the coefficient of PM10 were more robust. In other words, higher concentrations of SO2 and PM10 in the air have brought about a higher degree of ADL disability. These results demonstrate that an increased concentration of air pollutants aggravates the degree of ADL disability and that PM10 plays a more important role. The results of Model (4) show that air quality has a significant and negative impact on residents’ ADL disability; the worse the air quality is, the higher the degree of residents’ ADL disability. This result proves the robustness of the results of pollutant concentrations.

Table 3 Impact of air quality on ADL disability: Benchmark regression

In terms of control variables, population density, annual rainfall and annual average temperature had significant effects on ADL disability. Population density and annual rainfall had positive effects: the higher the population density and annual rainfall were, the lower the degree of ADL disability. On the other hand, annual average temperature had negative effects: the higher the annual average temperature was, the higher the degree of ADL disability. Regarding individual characteristics, household registration, depression, self-reported health and serious illness had positive effects on ADL disability, but marital status, disability, physical pain, gender and education had significant and negative effects on ADL disability.

These results demonstrate that the concentration of air pollutants has a significant impact on ADL disability, and among the control variables, the basic health status of individuals is the primary factor affecting ADL disability. Moreover, by looking into the marginal substitution effect of air quality and serious illness, to maintain the level of ADL disability, the decrease in ADLs caused by a 1% increase in SO2, NO2, PM10 and the AQI needs to be compensated by a 1.2325, 0.0346, 2.087, and 2.826% reduction in the serious illness, respectively. The substitution relationship between air quality and other health variables can also be investigated; however, they were not of interest to this study.

Marginal effect analysis

Based on Table 3, the marginal effect of air quality on ADL disability can be further estimated, and the results are shown in Table 4. Because the ordered logit model can only provide limited information on the signs and significance of parameters, it is necessary to estimate the marginal effect of air quality on ADL disability. When all explanatory variables are at the mean value, the influence of the exogenous explanatory variables can be expressed as Eq. (9):

$${left.frac{partial probleft( ADL=i/ Airright)}{partial Air}right|}_{Air=overline{Air}}left(i=1,2,3,4,5right)$$

(9)

Table 4 Marginal effect of air quality on ADL disability

Table 4 shows the marginal effects of air quality on the ADL disability of residents. PM10 is the primary factor affecting ADL disability, and when the PM10 concentration is increased by 1 unit, the probability of serious disability, severe disability, moderate disability, mild disability and healthy status of residents is significantly increased by 0.005, 0.02, 0.20, 0.79 and 1.94%, respectively. The marginal effect of NO2 is very weak and nonsignificant. In comparison, when the SO2 concentration was increased by one unit, the increase in the probability of serious disability, moderate disability and mild disability was 0.013, 0.12 and 0.45%, respectively, whereas the health reduction probability was − 1.10%. From the test of the marginal effect of the AQI, the above results are robust. The marginal effect of the AQI on severe, mild severe, moderate and mild disability is positive, and the marginal effect of the AQI on moderate and mild disability is higher. If the AQI is increased by 1 unit, the probability of moderate and mild disability increases by 0.30 and 1.15%, respectively. Meanwhile, the marginal effect of the AQI on health reaches 2.84%, which means that a 1 unit increase in the AQI leads to a 2.84% decrease in the probability of residents’ health.

Analysis of group heterogeneity

To investigate the variations in the impact of air quality on ADL disability between different groups, analysis models were stratified according to age, regional economy (GDP), gender and LTCI policy pilot. These results are shown in Table 5.

Table 5 Heterogeneity of ADL disability among different groups of residents affected by air quality

Regarding age, we used the elderly population with higher ADL disability risk as the division reference; thus, those aged 60 years and above were divided from others. The results show that compared with the age group under 60 years, air quality has a significantly higher impact on ADL disability of residents over 60 years. SO2 and PM10 have a significant impact on the ADL disability of residents over 60 years. This indicates that under the same conditions, the probability of ADL disability in elderly individuals brought by air quality deterioration is higher than that of the nonelderly population. However, there was no significant difference in the effect of the AQI on ADL disability by age.

In terms of regional economy, we selected the regional economic aggregate as the grouping standard; that is, the regional GDP lower than the average GDP was the low economic group, whereas the regional GDP higher than the average GDP was assigned to the high economic group. The results showed that compared with the low economic group, air quality had a more significant and negative effect on ADL disability in the high economic group. This is probably because the areas with stronger economies tend to promote better quality of life. Areas of strong economic development also have higher population density and more urban automobile pollution and industrial pollution, thus resulting in a significantly higher impact of air quality on ADL disability. In the low-level economic development area, the situation is the opposite. However, there was no significant difference in the effect of the AQI on ADL disability of different regional economic groups.

Moreover, compared with male residents, air quality had a more significant impact on ADL disability in female residents. This is because the life expectancy of female residents is generally higher than that of male residents, and in daily life, female residents are mainly engaged in household activities. Therefore, females experience more ADL disability related to cooking fume inhalation at home than males. However, the impact of the AQI on ADL disability was more significant for male residents since in general, workers in the mining industry are mostly men. Therefore, the impact of outdoor air pollution is higher for males, which increases the probability of ADL disability.

For the LTCI pilot group, the dummy variable of the pilot policy was constructed according to the implementation time of the LTCI policy in 15 pilot cities in 2016, whereby the nontreatment group and treatment group were determined. The results show that compared with the pilot areas, the air quality in the nonpilot areas had a more significant impact on ADL disability; that is, the LTCI pilot reduced the risk of ADL disability caused by air quality and promoted the prevention or rehabilitation of ADL disability among residents.

Analysis of the interaction between air quality and serious illness

Among the individual characteristics that affect ADL disability, serious illness was the most important factor. Previous theoretical research on LTCI shows that the disabled population is mainly affected by serious illnesses such as cerebral haemorrhage and cerebral infarction. Therefore, it is of great theoretical significance to investigate the interaction between serious illnesses and air quality. The test results of the interaction items are presented in Table 6. The interaction terms of serious illness and SO2 and the interaction of serious illness and NO2 play a significant and positive role in ADL disability, and the serious disease rate has a significant and negative effect on ADL disability. However, from Table 3, which shows the estimation results for the models without interaction items, the impact of serious illness on ADL disability was significantly positive, which is contrary to reality and theory. The results for Model (4) in Table 6 also show that the interaction terms have a positive moderating effect but are not significant.

Table 6 Estimation of the effects of the interaction between air quality and serious illness on ADL disability

The estimation results of the interaction terms suggest that air quality aggravated ADL disability caused by serious illness, and the interaction terms of serious illness and the concentrations of SO2 and NO2 were the main factors in the positive promotion effect on ADL disability. The primary reason for this might be that the increase in air pollutants increases the probability of residents suffering from serious illness, thus aggravating the risk of ADL disability.

Extensive analysis

The effect of air quality on ADL disability has been analysed. Furthermore, to fix the problems of self-selection bias and missing variables in samples, we used control samples and considered two-way fixed effects in a more robust model.

Bias processing of the self-selection sample

Due to the environmental migration in the process of air pollution, the estimation results are likely biased. To reduce the estimation bias caused by environmental migration, in the sample processing step, a subsample test was conducted for the participants whose residence location and groups did not change. The results are given in Table 7. It becomes clear that SO2 had a negative impact on ADL disability at the 10% significance level, NO2 had a negative impact on ADL disability at the 5% significance level, and the AQI had a negative impact on ADL disability at the 10% significance level. Therefore, the findings of previous models were robust.

Table 7 Effect of air quality on ADL disability of permanent residents

Treatment of bidirectional fixed effects of panel data

Although the above analysis synchronously controlled for the corresponding individual sociodemographic characteristics and urban environmental characteristics, missing variables might still exist and result in estimation bias. Therefore, we first used a two-way fixed effects model to address the endogeneity problem caused by missing variables. This was referred to by Liu and Hu [17], who viewed classified variables as continuous variables and employed a linear two-way fixed effects model. In this case, ADL disability was considered a continuous variable, and the test results for this model are presented in Table 8. As a result, SO2, NO2 and the AQI did not show a significant effect on ADL disability. The significance levels of SO2 and the AQI were decreased in the fixed effects model, but they were still significant at 15%. PM10 had a significant and negative effect on ADL disability at a significance level of 1%, and the significance of PM10 was higher than the results of the benchmark model. Therefore, air quality still had a significant impact on ADL disability in the panel two-way fixed effects model, which means that the result was robust.

Table 8 Impact of air quality on ADL disability: Based on fixed effects

Instrumental variables

We further adopted the instrumental variable method for endogenous processing. An ordered probit instrumental variable method was selected. According to previous studies, the abundance of regional mineral resources and the proportion of mining industry employees in the total population could be used as instrumental variables of air quality [17]. Therefore, we chose the proportion of mining industry employees in the total population as the proxy variable of regional mineral resources and constructed the two-stage method of IV for the ordered probit model. The results are given in Table 9.

Table 9 Estimation results of the IV ordered probit model

From the results of the first-stage test in Models (1) to (3), mineral resources have a significant and positive effect on air quality. The validity test of instrumental variables shows that the F value in the first stage is significantly greater than 10, indicating that the problem of weak instrumental variables did not exist. In other words, the selection of instrumental variables was effective. The results of second-stage tests in Models (1) to (3) show that air quality had a significant and negative impact on residents’ ADL disability at the significance level of 1%, which further demonstrates that the results of this study are robust. The results for Model (4) suggest that the AQI still had a significant and negative effect on ADL disability. This further proves that poor air quality significantly aggravates ADL disability. In addition, it can be seen from Model (4) that to keep ADL disability unchanged, ADL disability caused by a 1% increase in the AQI requires an 89.9652% reduction in serious disease to compensate for ADL damage. This means that the reduction amount of ADL disability brought by a 1-unit improvement in air quality equals the amount caused by a 89.9652-unit decrease in severe illness.

https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13240-7

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