Air pollution, residential greenness and medical expenditure in older adults

doi:10.21203/rs.3.rs-2085112/v1

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Air pollution, residential greenness and medical expenditure in older adults

https://doi.org/10.21203/rs.3.rs-2085112/v1

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Background: Environmental factors have been thought of as potential determinants for human health and would plausibly affect health expenditures. However, little is known about the association between air pollution, greenness, and their combined effect on medical expenditures, let alone, related evidence in older adults is rarely.

Method: We used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Medical expenditures were defined as out-of-pocket medical expenditures in the 12 months preceding the survey. A two-year average of PM2.5 concentrations prior to medical expenditures was collected as PM2.5 exposures. Greenness exposure was contemporaneous with PM2.5 and evaluated by residential units’ NDVI value in 250 m buffer. The two-part models were performed to assess the independent influences of PM2.5, greenness, and its combined influence. The first model was a logistic regression model based on the whole sample, called the participation models. Then a log-transformed linear regression model was fitted using samples with nonzero medical expenditures, named intensity models.

Result: We found that each 10-µg/m3 increment in PM2.5 increased the odds of incurring any costs (coefficient=0.80, P <0.001) according to the participation models. PM2.5 was associated with an increase in expenditure based on intensity models (coefficient=0.08, P <0.001). Each 0.1-unit increase in greenness decreased the odds of incurring any costs in the participation models (coefficient=-0.77, P <0.05). The risk of increasing medical expenditures attributed to PM2.5was higher in areas with a low level of greenness (coefficient=0.16, P <0.001). While greenness did not present a protective effect for increasing costs in the low level of PM2.5 (coefficient=0.21, P <0.05), increasing greenness could reduce costs in the high and medium level of PM2.5. Uneducated participants and others without pensions were vulnerable to the combined effect.

Conclusion: Air pollution is a potential source of medical expenditures; in comparison, expanding greenspace is associated with these societal costs saved. Suppose the combined impact of greenness and PM2.5 was to be replicated in a future study, which would improve the utility of current evidence.

Elderly

Green space

Air pollution

Medical expenditures

Panel

China

The past decades have been marked by a significant increase in the ageing population worldwide. At present, the increment in the global ageing population is prominent speeding up. Scholars estimated that about 22% of the world's population will be over 60 years of age by 2050 [1]. Ageing is associated with a longer lifetime, higher chronic disease prevalence and functional disability; thus, it implies a potential increase in medical spending [2]. Many countries have sought to address such a large medical burden that an aging society face. In this context, researchers have identified factors affecting medical expenditures, such as medical demand and health status. Beyond that, residential environment factors such as exposure to air pollution, especially for PM_2.5 (particulate matter < 2.5 mum in diameter), potentially contributed to an increase in healthcare expenditures also and have received extensive attention [3–7]. For instance, the global economic costs of deaths attributed to exposure to PM_2.5 was $4.09 trillion (95% CI: 3.19–5.05); among them, the older population contributed about 59% (95% CI: 59–60) of the total costs [8]. 41 Organization for Economic Co-operation and Development (OECD) countries reported that PM_2.5 was associated with a health burden of $5.1 trillion [9]. From the micro point of view, results from two Chinese studies suggested that exposure to PM_2.5 also increased households’ medical expenditures [10, 11]. Other studies found that exposure to PM_2.5 was positively associated with high medical expenditures for some specific health problems such as respiratory ailments [12–14], cardiovascular diseases [15] even psychotic and mood disorders [16]. Although the current studies relied on real-world data and showed a considerable source of increment in medical expenditure is PM_2.5, knowledge about this association on the elderly are very scant.

In addition to PM_2.5, another important environmental indicator- greenness, has recently addressed beneficial health effects. A growing body of evidence reported that exposure to greenness concerning various and beneficial health outcomes such as lower odds of obesity, cardiovascular disease, natural mortality; contact with green space also linked to improved activity function of the elderly [17–21]. Furthermore, greenness has a positive impact on the subject health status and psychological well-being of individuals [22]. These results are important because greenness is associated with health indirectly through many pathways, such as promoting physical activity and releasing mental stress. Thus, benefits from greenness also can affect medical expenditures in older age is plausible. Based on such background, scholars pointed out that expanding green space could bring a positive return on investment compared with the cost when considering health in cost-benefit analyses [23]. Thus, exploring the association between greenness and health economic outcomes will be an important next step in environmental epidemiology. However, comparatively few epidemiological studies have attempted to quantify the benefit of greenness in reducing medical expenditures.

It should note that the health influence attributed to these environmental factors did not appear an independent effect of one another [20, 24–27]. This is a considerable aspect since these factors are highly correlated, such as, in theory, areas with heavy air pollution, general with a lower density of vegetation coverage, that can offset the benefit of greenness [23, 28, 29]. In turn, greenness can eliminate hazardous materials exposure (i.e., mitigate the exposures to noise, heat, air pollution et al.) and change the response to air pollution [18] have been well documented. Hence, this evidence suggests that greenness can modify the effect of PM_2.5 on medical expenditure and vice versa is plausible. However, whether air pollution and greenness show a combined effect on medical expenditures is still an unanswered question which requires further investigation.

Medical cost is a widely-used and important health-related indicator, especially for the elderly; which directly reflects their comprehensive health status [30]. Also, the elderly’s health profile is quite concerning with a three-fold increment in medical expenditures compared with the last decade [31]. Suppose the association between greenness, PM_2.5 and medical expenditure is sufficiently and early quantized and understood. In that case, policies based on such timely evidence may yield better strategies to cope with health-related challenges aroused by aging and helps save economic costs caused by the delayed mitigation response. Therefore, this study aims to use national and representative panel data across China from 2008 to 2014 and attempt to assess the independent effect of PM_2.5 and greenness on medical expenditures among the elderly after adjusting potential covariates. Furthermore, we fill the research gap by examining how their mutual correlations affect medical expenditures.

Study data

Panel data on 26247 participants collected from 2008, 2011, and 2014 waves of the CLHLS were used. The CLHLS was a national investigation that enrolled elderly aged over 65 years from 23 of the 31 provinces/municipalities/autonomous regions across China. The project's primary objective is to study the influencing factors of healthy longevity. In particular, these provinces/municipalities/autonomous regions covered approximately 85% of the population in China. Estimated in 1998, this project has conducted in the 2002, 2005, 2008, 2011, 2014, 2018 waves. New participants from nearby residences were recruited in each interview to replace the elderly who died or lost follow-up. A detailed illustration of the CLHLS was published elsewhere [6, 32]. Given the privacy protection, the database did not provide each participant's home address; the residence information was only available at the district or county level [33]. In summary, we used data from the 2008, 2011, and 2014 waves of the CLHLS because the unique residential unit (district/county) geocodes could be identified. In each wave, we excluded participants aged under 65 years, with missing information on the residential unit's information, PM_2.5 assessments, greenness assessments, and demographic characteristics. Finally, 26247 participants from 802 residential units (district/county) with complete information from three waves were collected in the paper (Figs. 1 and 2). The CLHLS was approved by the Institutional Review Board, Duke University (Pro00062871), and the Biomedical Ethics Committee, Peking University (IRB00001052-13074). Written informed consent was received from all participants.

Medical expenditures

As a previous study did, information on out-of-pocket medical expenditures in the last year before the interview was derived from the individual's questionnaire and set as the study's dependent variable [11]. The information on medical expenditure is collected via a standard questionnaire consistent across the different survey waves. Specifically, these medical expenditures are occurred in the 12 months preceding the survey interview and set by the question, "how many medical expenses paid by you and your family last year out of total medical costs?".

PM_2.5 assessment

In the current study, annual average of PM_2.5 concentration in residential units (at district or county level) of the subjects resided was derived from an open public database built by the Atmospheric Composition Analysis Group from the University of Washington [34]. Researchers used satellite combining Aerosol Optical Depth (AOD) retrievals from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) to report ground-level of PM_2.5 concentrations with 1.1*1.1 km resolution. Many studies reported long-term exposure to PM_2.5 was strongly associated with elderly’s mortality, herein, long-term exposure as defined as more than one-year of exposure [35, 36]. Referring to the previous study [37], the assessment of PM_2.5 was identified by considering multiple windows of exposure. Thus, we explored the association between the same period/one/two years average of PM_2.5 prior to medical expenditure occurred and medical expenditure. We finally found two-year average of PM_2.5 prior to medical expenditure occurred had the greatest impact on medical expenditure (Table S1).

Greenness assessment

Rely on multispectral remotely sensed data, on satellite-based and cloud-free vegetation index: normalized difference vegetation index (NDVI) was used to reflect greenness exposure in the study. NDVI is a general indicator of overall greenness and widely used in the field of medicine and epidemiology. It is recorded per 16-day at a different spatial resolution of 250, 500, and 1000 m et al. The NDVI was extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the National Aeronautics and Space Administration’s Terra Satellite. Because greenness exposure accurate to 250 m buffer is considered a suitable distance of greenness exposure with high reliability and for the elderly [17], which was used to assess the greenness. NDVI value ranged from − 1 to 1, and a larger value means more greenness coverage. All data can be downloaded from Google Earth Engine (https://earthengine.google.com/) and matched to each individual via ArcGIS 10.6 (ESRI, Redlands, CA, USA). In this paper, information of greenness exposure was a contemporaneous time of PM_2.5 exposures. This period also had the greatest impact on medical expenditure (Table S1). Namely, the greenness exposure was averaged for the two years before the medical expenditure occurred. Owing to the privacy protection provided by the CLHLS, the database did not provide the accurate home address of the participants. Thus, the greenness exposure defined as NDVI value in each residential unit (distract/county) the participants resided.

Covariates

Information on covariates was obtained from the individual's questionnaire. Study covariates included: age (< 89 ≥ 89), gender (female, male), residence (rural/town, urban), pension (yes, no), education attainment (year), marital status (married, not married (separated, divorced, or widowed or never married)), smoking status (yes, no), drinking status (yes, no), exercising (yes, no); self-reported cardiovascular diseases including hypertension, heart diseases, cerebrovascular diseases (yes, no), self-reported respiratory diseases (yes, no), enjoy social medical insurance (yes, no) and serious bedridden and need hospitalization within the past two years (yes, no).

Statistical analysis

Descriptive analysis

In the first step, we conducted descriptive analysis; all variables are shown as the mean (standard deviation, SD) or median (interquartile range, IQR) for continuous variables and number (percentage) for categorical variables.

Main model

Given the medical costs data are semicontinuous and highly skewed and about 23.5% of them were zero Yuan, in this panel database, the two-parts models were used to explore the association between PM_2.5 and greenness exposures and medical expenditure. The two-part model allowed us to first estimate the probability of having zero versus positive medical expenditures [38, 39]. To be specific, in the first model, a logistic regression model, called “participation model”, was applied to evaluate the association between each 10-unit increase in PM_2.5 and each 0.1-unit increment in greenness and medical expenditure with the probability of medical services utilization; the sample included participants with zero and nonzero expenditures. In the second model, we used a log-transformed linear regression model, called “intensity model”, to examine the associations of each 10-unit increase in PM_2.5 and each 0.1-unit increment in greenness with medical expenditures among participants only have positive medical expenditures. We also add the quadratic term of the PM_2.5/NDVI to test for nonlinear effect of them. To investigate the combined association between PM_2.5, greenness and medical expenditures, we introduced an interaction term between PM_2.5 and greenness in the model and stratified the analyses. In this part, greenness/PM_2.5 was categorized into tertiles (< 33%, 33% to < 66%, and ≥ 66%) to obtain sufficient data within. Corresponding, each category was defined as low/medium/high level of values, respectively. We analyzed the association between greenness/PM_2.5 and medical expenditures by the level of PM_2.5/greenness. Meanwhile, subgroup analysis was conducted to identify which groups were vulnerable to the independent of greenness/PM_2.5 exposures and the combined effect of them, herein, the analyses were stratified by gender, and individual socioeconomic status residence (education, pension and marital status). P values for difference were identified by adding a product term between greenness/PM_2.5 and modifier in the models [29]. Finally, we using subset that excluding participants only be interviewed for one time to identify how the selection of samples changed the estimated association.

Additional analysis

We further explored the potential non-linear association between greenness/PM_2.5 and medical expenditures by adding a quadratic term of greenness/PM_2.5 into the models (Table S5). We also considered conducting the mechanism analysis; namely, we suspected the risks of increasing medical expenditures for cardiovascular or respiratory diseases could be affected by air pollution/greenness (Table S6). Here, we selected participants with cardiovascular or respiratory diseases, had medical expenditures as a sample subset, and reported whether greenness/PM_2.5 affected medical costs.

Referring to prior studies, all of analysis were adjusted for covariates and used the xtlogit model to estimate the participation model and the xtreg model to analyses the intensity model [40, 41], respectively. Given the characteristics of panel data, random-effects or fixed-effect models were performed to the whole analysis basing on Hausman tests. The estimated coefficients (β) and its standard errors (SE) were reported. The analysis was performed by Stata SE 17.0 software (Stata Corp LP, College Station, TX, USA). All tests were two-sided and a p-value < 0.05 reveals statistical significance.

Descriptive analysis

Table 1 indicates the demographic characteristics of the sample. Overall, 26247 participants aged over 65 years were included in the study. The mean age was 87.4 (SD = 11.4), 85.0 (SD = 11.1), and 84.5 (SD = 10.5) in the 2008, 2011, and 2014 waves, respectively. The demographic characteristics of the participants in the three waves were generally analogous. More than half of the elderly were females or had not a pension. Less than 20% of them were smokers, drinkers, or had self-reported respiratory diseases. According to Table 1, the median of medical expenditures was increased from 300 in the 2008 wave to 400 in the 2011 wave and sustained 300 Yuan in the 2014 wave. Regarding PM_2.5, the three waves’ median and IQR were 51.4 (24.0), 54.0 (26.9) and 51.8 (26.2), respectively. The median and IQR of NDVI value was 0.46 (0.15), 0.45 (0.16), and 0.44 (0.16) in three waves, respectively.

Table 1

Descriptive Statistics for categorical variables n(%)/median(IQR)
Variable	2008 (n = 15157)	2011 (n = 6809)	2014 (n = 4281)
Age
< 89	7421 (49.0)	4120 (60.5)	2777 (64.9)
≥ 89	7736 (51.0)	2689 (39.5)	1504 (35.1)
Gender
Male	6430 (42.4)	3051 (44.8)	1985 (46.4)
Female	8727 (57.6)	3758 (55.2)	2296 (53.6)
Education attainment
Uneducated	9572 (63.2)	3920 (57.6)	2396 (56.0)
Educated	5585 (36.8)	2889 (42.4)	1885 (44.0)
Pension
No	12416 (81.9)	5491 (80.6)	3411 (79.7)
Yes	2741 (18.1)	1318 (19.4)	870 (20.3)
Marital status
Married	4606 (30.4)	2616 (38.4)	1765 (41.2)
Not married	10551 (69.6)	4193 (61.6)	2516 (58.8)
Residence
Urban	3098 (20.4)	1219 (17.9)	684 (16.0)
Rural/town	12059 (79.6)	5590 (82.1)	3597 (84.0)
Smoking status
Yes	2585 (17.1)	1224 (18.0)	757 (17.7)
No	12572 (82.9)	5585 (82.0)	3524 (82.3)
Drinking status
Yes	2577 (17.0)	1167 (17.1)	705 (16.5)
No	12580 (83.0)	5642 (82.9)	3576 (83.5)
Exercising
Yes	4108 (27.1)	2342 (34.4)	1235 (28.8)
No	11049 (72.9)	4467 (65.6)	3046 (71.2)
Self-reported cardiovascular diseases
No	10688 (70.5)	4352 (63.9)	2651 (61.9)
Yes	4469 (29.5)	2457 (36.1)	1630 (38.1)
Self-reported respiratory diseases
No	13427 (88.6)	6054 (88.9)	3835 (89.6)
Yes	1730 (11.4)	755 (11.1)	446 (10.4)
Serious bedridden and need hospitalization within the past two years
No	14985 (98.9)	6717 (98.6)	4198 (98.1)
Yes	172 (1.1)	92.0 (1.4)	83.0 (1.9)
Enjoy social medical insurance
No	11320 (74.7)	3345 (49.1)	1046 (24.4)
Yes	3837 (25.3)	3464 (50.9)	3235 (75.6)
Medical expenditures (Yuan)
	300 (950.0)	400 (2000.0)	300 (1500.0)
NDVI
	0.46 (0.15)	0.45 (0.16)	0.44 (0.16)
PM_2.5 (µg/m³)
	51.4 (24.0)	54.0 (26.9)	51.8 (26.2)

Regression results of the independent influence of PM_2.5/greenness on medical expenditures

Table 2 shows the results of the two-part models for examining the independent influence of PM_2.5 on medical expenditures. In the fully adjusted double-exposure participation models, each ten-µg/m3 increment in PM_2.5 increased the likelihood of incurring any medical expenditures (coefficient = 0.80, P < 0.001). Each ten-µg/m3 increment in PM_2.5 was associated an increased risk of nonzero medical expenditures, as demonstrated by the intensity models in the two-part model (coefficient = 0.08, P < 0.001). The sensitivity analyses did not change the results notably (Table S3). In the subgroup analysis (Fig. 3), we observed the relationship between PM_2.5 and medical expenditures was not modified by individual characteristics (P for interaction all > 0.05).

Table 2 also lists the results of two-part models for each 0.1-unit increment in greenness and medical expenditures. After adjusting for covariates, in the double exposure participation models, we could see exposure to greenness decreased the risk of incurring any medical expenditure (coefficient=-0.77, P < 0.05). Whereas, according to the intensity models, not significant links of greenness with medical expenditures was detected (coefficient = 0.04, P > 0.05). We did not observe that the sensitivity analyses change the results substantially (Table S3). We also performed a subgroup analysis to identify the vulnerable population but not significant results were detected (P for interaction all > 0.05 and see Fig. 3). When we add the quadratic term of the PM_2.5/NDVI to test for nonlinear effect of them. As Table S5 shows, both in the participant and intensity model, the quadratic term of the PM_2.5/NDVI was not significant. Thus, no sufficient results could support the possible nonlinear association between PM_2.5/NDVI and medical expenditures in our study. The result of mechanism analysis (Table S6) indicated greenness was associated with lower odds of any medical costs occurring among those who had respiratory diseases and medical expenditure (coefficient=-0.04, P < 0.001).

Table 2

Two-part models for each 10-unit increment in PM_2.5 and medical expenditures
Variables	Participation model			Intensity model
Variables	Single-exposure model ^a	Single-exposure model ^a	Double-exposure model ^b	Single-exposure model ^a	Single-exposure model ^a	Double-exposure model ^c
PM_2.5	0.81***		0.80***	0.08***		0 .08***
	(0.14)		(0.14)	(0.02)		(0.02)
Greenness		-0.77 **	-0.77**		0.04	0.04
		(0.32)	(0.33)		(0.05)	(0.05)
FE model	YES	YES	YES	YES	YES	YES
Observation	26,247	26,247	26,247	20,069	20,069	20,069
Hausman test	200.93***	186.84***	203.61***	215.15***	200.38***	215.75***

Note:

***/**/* Statistically significant at the 1%/5%/10% level.

^a Adjusted for age, sex, residence, pension, marital status, smoking status, drinking status, exercising, cardiovascular diseases, enjoy social medical insurance, self-reported respiratory diseases, education attainment, and bedridden all the year around.

^b Single-exposure model + further adjusted for NDVI.

^c Single-exposure model + further adjusted for PM2.5.

The combined effect of PM_2.5 and greenness on medical expenditures

Figure 4 summarizes two-part regression results of the combined effect of PM_2.5 and greenness on medical expenditures. We also listed the distribution of medical expenditures in PM_2.5/greenness categories in tertiles (Table S2). After controlling for related confounding variables, we could see the interaction term of PM_2.5 and greenness (i.e., NDVI in 250 m buffer categories in tertiles) was statically significant (P-value for interaction was 0.006) in the intensity models. Which implied the effect of PM_2.5 on medical expenditures was less in greener areas (coefficient = 0.00, P > 0.05 for high level of greenness; coefficient = 0.06, P > 0.05 for medium level of greenness; coefficient = 0.16 P < 0.001 for low level of greenness). Also in the intensity models, we found the effect of greenness on medical expenditures was modified by PM_2.5 level (coefficient=-0.04, P > 0.05 for high level of PM_2.5; coefficient=-0.04, P > 0.05 for medium level of PM_2.5; coefficient = 0.21, P < 0.05 for low level of PM_2.5). Figure S1 describes the sensitivity analysis for the combined effect of PM_2.5 and greenness on medical expenditures, and the results were consistent with above analysis.

In the subgroup analysis, we first examined the significance of interaction term of subgroup (Table S4). We found a significant association between PM_2.5 and medical expenditures modified by greenness among participants had not pension using intensity model (Figure S2). We also observed the association between per 0.1-unit increase in greenness and medical expenditures was modified by the level of PM_2.5 among uneducated population (Figure S2).

Additional analysis

We did not found the potential non-liner association between greenness/PM_2.5 and medical expenditures (Table S5). As for the part of mechanism analysis, we only found increasing greenness was associated with lower risks of medical expenditures (coefficient=-0.04, P < 0.001) among participants had respiratory diseases (Table S6).

Our study provides new insights into the potential economic influence of PM_2.5 and greenness; the study results help quantifies these associations relied on a real-world database. Our findings showed that PM_2.5 and greenness were both independently associated with medical expenditures. As anticipated, greenness modified the effect of PM_2.5 exposures on medical expenditures. Additionally, exposure to PM_2.5 also affected the influence of greenness on medical expenditures.

In our study, the first finding was that exposure to PM_2.5 would lead to increased medical expenditures, which was corroborated by previous surveys focused on adults [42–46]. According to the participants models, a ten-µg/m³ increment in PM_2.5 was associated with 2.23 times (exp (0.80) = 2.23) the possibility of incurring any medical expenditures, the magnitude of estimation was 1.08 times (exp (0.80) = 1.08) in the intensity models; these results were higher than the previous study based on individual microdata from the China Urban Household Survey (UHS) Database and pointed out that increased exposure to PM_2.5 contributed to a 2.94% (95%CI: 1.08%-4.80%) increase in household healthcare expenditure [10]. This finding suggested that the economic impact may be heavier among older adults than younger adults. In particular, our study further revealed the long-term and lagged impact of air pollution (i.e., two years before expenditure occurred had the most significant impact on costs) on medical cost consumption, indicating a lasting risk of increased medical burden attributed to air pollution. Consequently, our findings emphasized the importance of improving air quality. We further observed that the association between PM_2.5 and medical expenditures was significant in participation and intensity models. The coefficients were relatively higher in participants with nonzero and zero expenditures than in others without zero medical expenditures, which reflected that exposure to PM_2.5 greatly impacted the whole population and suggested a detailed protection strategy for healthy and ailing elderly may be equally vital.

The second finding of our study was that each 0.1-unit increment in greenness was associated with the lower odds of incurring any medical expenditure in participants with zero and non-zero costs (i.e., participation model). In contrast, no significant association between greenness and non-zero medical expenditures (i.e., intensity model) was detected in the present study. Currently, many studies have discussed the relationship between greenness and various health outcomes, yet, these researches have never explored whether green space affect comprehensive medical expenditures. Lots of literature reported an adverse impact of PM_2.5 on medical expenditure, which could provide many intersecting conclusions for our study. However, there are differences in the parallels. Because greenness has been associated with a wide range of health benefits, and the effect is protective [20]. Actually, the negatively association between greenness and medical expenditure is plausible. Because according to the green environments-health hypothesis, natural green environment is considered as natural filters that reduce hazardous materials exposure; it also provide restorative, stress-relieving spaces to individual and provide attractive space to promote physical activity [22, 47, 48]. It is noted that activity ability act as an important mediator in the relationship between greenness and health outcomes [24], which can partly explain why there are no significant links between greenness and expenditure among participants had medical costs. Namely, we speculated that diseases or limited activity of daily living limit their ability to contact with natural environment to the same extent. Overall, given an estimation was − 0.77 (i.e., exp = 0.46), it is estimated that 60% reduction of medial expenditures attributed to greenness approximately. This is substantial at the population level considerably, especially in China, one of the fastest-aging countries worldwide. Thus, our findings suggest that an innovative public health intervention – greenspace construction should be considered to lower medical burden. It is not only a vital component of residential planning but also a cost-effective strategy for lasting health benefits [49].

Our study observed that exposure to PM_2.5 significantly increased medical expenditures in a low level of greenness. Specifically, the adverse impact of PM_2.5 on medical expenditure increases as greenness continues to decrease. Moreover, participants who had no pension were vulnerable to such joint effects. These conclusions display rationality because the vegetation is more likely to grow in an environment with good air quality; besides, it can trap fine particulate matter released from polluted air and clean the air quality in the immediate vicinity [47, 48, 50]. Additionally, greenness decreases urban heat and moderates ambient air temperature; it is important to facilitate social cohesion and public health [51]. However, according to the intensity models, greenness appeared to have a hazardous influence on medical expenditure in a low level of PM_2.5. We thought it should not be over-interpreted, and we did not by mean that greenness is a risk factor for increasing medical expenditures. Some explanations included: first, we could see the lower limit of 95%CI was very close to null; it told the result was not entirely steady. Second, along with the higher level of PM_2.5, the benefit of greenness was attenuated, yet, unfortunately, coefficient estimations of these results overlapped the null. Third, many confounders might influence our assessments, such as duration of time in outdoor activities and distance to greenspace et al., but these information was unavailable in the present study. Hence, our findings should be confirmed and repeated in future investigations, and a future in-depth study to understand the mechanisms of interactive influence between greenness and air pollution on health outcomes is warranted. More so, expanding vegetation can aid in cleaning the air environment, and improving the public’s well-being is still not denied.

When we examined whether the association between increment in PM_2.5 and medical expenditure was modified by the level of greenness in different sub-populations, we found that stronger relationship in the elderly without pensions, that is, in the low level of greenness, exposure to PM_2.5 was related to increased medical costs. Furthermore, we observed the influence of greenness on medical expenditures was modified by PM_2.5, and uneducated participants were vulnerable to such effect. In short, compared with the medium level of PM_2.5, the benefit of greenness was attenuated at the high level of PM_2.5, although the estimated coefficient of the high-level group was unexpected. Elderly without pension and uneducated were associated with a low level of individual socioeconomic status (SES); prior studies have reported the association between PM_2.5 or greenness and health outcomes were modified by SES, which was consistent with our finding [52, 53]. Thus, as a leader, the policymakers should not only make an effort to reduce environmental risk factors exposure among the crucial population but also enhance their availability for greenspaces.

The mechanism analysis showed one significant linkage between greenness and lower odds of increasing medical expenditures among participants with self-reported respiratory diseases. This finding was consistent with our primary analysis and as a different aspect of our study, which also indicated the benefit of greenness on a single disease and expanding greenspace may help save related disease burdens. However, the result is a preliminary exploration; we should explain it cautiously.

The present study has its innovations and contributions to health, such as using microdata from a national-scale investigation with large sample size and using two-part models to overcome the estimation error caused by zero medical costs. Overall, our findings should be interpreted as the social cost of PM_2.5, greenness and their combined influence. These quantitative conclusions provide novel insight into understanding the comprehensive impact on economic development. Despite it, our study embeds some limitations and should be critically recognized. First, owing to privacy protection, in the CLHLS, the participants' home addresses were unavailable. In the present study, we estimated the environmental exposure at the district or county level. Such a measurement approach was too crude not to reflect the real exposure status and would result in degrees of measurement errors to some extent. Nevertheless, satellite-based assessment improved exposure accuracy and was widely used in prior studies [54]. Second, the effect of greenness and PM_2.5 exposure depends on human behaviour and outdoor activity (i.e., frequency and intensity) to a certain extent, but these data were not available in the database. Third, greenness composition (e.g., structure and types of green environment) and quality of greenness should be considered for the topic and affect the study results. Although the current study conducted mechanism analysis, detailed medical expenditures for cardiovascular or respiratory diseases, including treatment, diagnosis, and hospitalization, were unavailable. Thus, further investigation should acquire more data to illustrate the mechanism for the association between air pollution/greenness and medical expenditures. Lastly, since our sample involves a large proportion of the oldest-old (i.e., over 80 years) and our study results are insufficient for causal inference, verifying our study's generalization and the underlying causality in future investigation is necessary.

Currently, the ageing population, increased medical burdens, polluted air environment, and decreased greenspace were common problems in China; however, these challenges were not only faced by China but also occurred in most countries worldwide. Likewise, developing effective policy and planning requires evidence-based assessment according to real-world data. Under this background, the study suggested that similar countries facing consistent problems might attempt to minimize medical expenditure based on a range of environment-targeted strategies, including clearing the air environment and better designing, allocating and expanding greenspaces. Especially for the areas with high levels of air pollution, it is urgent to expand green space coverage and promote the function of green space. In addition, it also needs to consider residents' features during land resource allocation during the policy implementation process.

The present study provided a novel view of the potential beneficial impacts of greenness exposure on medical expenditures among the elderly and identified that exposure to PM_2.5 induced an increment in expenditures. We also underlined a combined association between greenness and PM_2.5 on medical expenditures. Some sub-populations with relatively low SES should be considered vulnerable populations and provided more resources for them. Considering the dramatic increment in the number of seniors, a strategy for expanding green space and reducing the concentration of air pollutants should be developed to promote healthy aging.

Ethics approval and consent to participate

The Research Ethics Committees of Peking University and Duke University granted approval for the Protection of Human Subjects for the Chinese Longitudinal Healthy Longevity Survey, including the collection of the data used for the present study. The survey respondents provided informed consent prior to their participation.

Declaration of competing interest

The authors declare they have no actual or potential conflict of interest.

Funding

This research was financially supported by the National Key R&D Program of China (2021YFC3001000), National Natural Science Foundation of China (Grant No. U1911204, 51861125203).

Availability of data and materials

Data can be downloaded at website: https://opendata.pku.edu.cn/.

Consent for publication

Consent forms will be available upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

Wensu Zhou and Fenfen Zhou: Data cleaning; Wensu Zhou, Wenjuan Wang, Fenfen Zhou, Rui Li, and Li Ling: Conceptualization; Wensu Zhou, Rui Li, and Wenjuan Wang: Data curation, writing, original draft preparation, methodology, software, reviewing; Wensu Zhou, Rui Li, Fenfen Zhou, Wenjuan Wang, and Li Ling: Visualization. Wensu Zhou, Fenfen Zhou, Li Ling, and Wenjuan Wang: Supervision.

Acknowledgements

The authors wish to thank the participants of the CLHLS. The author would like to thank Fan Chaonan for her time and guidance throughout the revision of this paper.

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No competing interests reported.

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Air pollution, residential greenness and medical expenditure in older adults

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Version 1

Abstract

Figures

Introduction

Method

Study data

Medical expenditures

PM_2.5 assessment

Greenness assessment

Covariates

Statistical analysis

Descriptive analysis

Main model

Additional analysis

Result

Descriptive analysis

Regression results of the independent influence of PM_2.5/greenness on medical expenditures

The combined effect of PM_2.5 and greenness on medical expenditures

Additional analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Air pollution, residential greenness and medical expenditure in older adults

Status:

Version 1

Abstract

Figures

Introduction

Method

Study data

Medical expenditures

PM2.5 assessment

Greenness assessment

Covariates

Statistical analysis

Descriptive analysis

Main model

Additional analysis

Result

Descriptive analysis

Regression results of the independent influence of PM2.5/greenness on medical expenditures

The combined effect of PM2.5 and greenness on medical expenditures

Additional analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

PM_2.5 assessment

Regression results of the independent influence of PM_2.5/greenness on medical expenditures

The combined effect of PM_2.5 and greenness on medical expenditures