Abstract
Background
A series of studies have explored the health effects of long-term exposure to ambient PM2.5 among older adults. However, few studies have investigated the adverse effect of long-term exposure to ambient PM2.5 on frailty, and the results are inconclusive. This study sought to investigate the associations between long-term exposure to ambient PM2.5 and frailty in 6 low- and middle-income countries.
Methods
We included an analytical sample of 34 138 individuals aged 50 and older from the Study on global AGEing and adult health Wave 1 (2007/2010). Air pollution estimates were generated using a standard methodology derived from Moderate Resolution Imaging Spectroradiometer observations and Multiangle Imaging Spectroradiometer instruments from the Terra satellite, along with simulations from the GEOS-Chem chemical transport model. A 3-level hierarchical logistic model was used to evaluate the association between frailty index and long-term PM2.5 exposure at 3 levels (individual, province, and country).
Results
In rural areas, each 10 μg/m3 increase in ambient PM2.5 was associated with a 30% increase in the odds of frailty (OR = 1.30, 95% CI: 1.21–1.39) after adjusting for various potential confounding factors. The gender-stratified analysis showed that the association seemed to be slightly stronger in men (OR = 1.31, 95% CI: 1.18–1.46) than in women (OR = 1.21, 95% CI: 1.07–1.36) in rural areas.
Conclusion
In a large sample of community-based older adults from 6 middle-income countries, we found evidence that long-term PM2.5 exposure was associated with frailty in rural areas.
Keywords: Air pollution, Ambient PM2.5, Frailty, Older adults
A growing body of literature supports the adverse effect of long-term ambient air pollution exposure on lung function and other health outcomes (1,2). In older adults and susceptible individuals, short- and long-term exposures to air pollution have been associated with poor lung function, mortality, and cancer (3,4). Compared to other age groups, older adults are more susceptible to the impact of air pollution. At the same time, this population is also more susceptible to increased vulnerability to risk factors leading to frailty (5).
Given the current global trends of demographic aging, economic development, and urbanization, air pollution and frailty among older people have become emerging and intertwined public health issues. For example, individuals with compromised lung function do not have sufficient normal pulmonary defense mechanisms to respond to air pollution and particulate matter (6,7). Consequently, when the individuals experience an exacerbation of chronic obstructive pulmonary diseases due to pneumonia comorbid, they might become transiently frail or become frailer for those with existing frailty due to their inability to cope with the additional stress posed by air pollution. The situation can lead to poorer health outcomes and manifest as cardiovascular diseases or depression (8,9).
While a series of studies have explored the health effects of long-term exposure to ambient fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) among older adults, these studies generally assess the association between exposure to PM2.5 with a single disease endpoint, such as the incidence of acute coronary event (10) or cognitive performance (11), rather than a complex outcome such as frailty that is usually measured as an index generated from multiple indicators. Though no single-standard assessment tool of frailty is available, the frailty index has been well tested in several populations (12). To the best of our knowledge, only one study has specifically investigated the relationship between air pollution and the incidence of frailty assessed by the frailty index (9). The study reported that PM2.5 exposure was associated with an increased risk of developing frailty among patients suffering from myocardial infarction (OR = 1.53; 95% CI: 1.22–1.91, when comparing the 75th vs 25th percentiles of the exposure distribution), after adjusting for sociodemographic and clinical variables. However, a similar association has not been reported in a population-based study.
This article aims to examine the association between long-term exposure to PM2.5 and frailty among older adults in 6 middle-income countries (MICs) who participated in the Study on global AGEing and adult health (SAGE) during 2007–2010. We further evaluate the dose–response association between PM2.5 concentrations and frailty stratified by urban/rural settings.
Method
Study Population
The study population was drawn from the SAGE Wave 1 (2007–2010), a longitudinal cohort survey of aging and older adults in 6 MICs: China, Ghana, India, Mexico, Russian Federation, and South Africa. SAGE employed multistage cluster sampling strategies in all countries (13). The strata selected were defined by provinces/states and locality (urban/rural). Enumeration areas constituted the primary sampling units and were selected using the probability proportional to size method. Finally, household enumerations were randomly selected as the final sampling unit. The sampling design in Mexico was similar to the other 5 countries but included a supplementary, random sample from urban and rural households to compensate for the overrepresentation of metropolitan strata. Face-to-face interviews were used to complete the questionnaires. The response rates ranged from 51% in Mexico to 93% in China (India 68%, Ghana 80%, Russia 83%, and South Africa 77%).
Ethics and Consent
WHO’s Ethical Review Committee approved SAGE (RPC146). Additionally, each country obtained ethical clearance through their respective review bodies, and SAGE obtained written informed consent from each respondent.
Study Variables
Outcome variable—the frailty index
Frailty was defined as the accumulation of deficits (14). In this study, the frailty index was constructed as the proportion of deficits present in 40 variables (15). The index included variables on medical symptoms, medically diagnosed conditions, activities of daily living, and performance tests (walking speed and grip strength). The likelihood of frailty increases with more decrements in health (16). The complete list of variables and the coding is presented in Supplementary Table 3. The individual frailty scores ranged from 0 (no deficits) to 1 (highest level of deficits in all variables). A cutoff value of 0.2 has been recognized as a threshold for approaching a frail state (17,18). Hence, we classified individuals into not frail (0 to less than 0.2) and frail (0.2–1.0) groups.
Exposure variable—air pollution assessment
This study estimated the ambient PM2.5 levels using remotely sensed data. These estimates were derived from a combination of observations from the Moderate Resolution Imaging Spectroradiometer (19) and Multiangle Imaging Spectroradiometer instruments from the Terra satellite, along with simulations from the GEOS-Chem chemical transport model (www.geos-chem.org). The analysis resulted in estimates of the average long-term level of exposure to PM2.5 at an approximate resolution of 10 km × 10 km. According to a previous validation study, the estimated PM2.5 data had an expected 1-sigma uncertainty of 1 μg/m3 + 25% (20).
The location of respondents was geocoded using coordinates from the SAGE data sets and mapped to Google Earth at the community level. We refer to these as community addresses to indicate the varying size and meaning of the aerial unit to which each address was geocoded. A corresponding PM2.5 concentration estimate was assigned and used in the regression models. The “community” in our study refers to the township in China, enumeration block in Ghana and South Africa, village and enumeration block in India, a geostatistic area in Mexico, and atenum in Russia. We obtained the 1–5 years averaged PM2.5 concentration estimate before SAGE Wave 1 with this method. We used the 3-year averaged PM2.5 concentration in the main models. Considering the effect of temperature and humidity on the length of time people are exposed to PM2.5 outdoors, we also collected the 1–5 years averaged temperature and humidity for each “community.”
Other covariates
Other variables used in the analyses included age, gender, education, wealth quintiles, tobacco use, vegetable and fruit intake, and physical activity levels. According to the International Standard Classification of Education, education was divided into 5 levels. Wealth quintiles were derived from the household ownership of durable goods, dwelling characteristics (floor, wall, and stove), and access to services such as clean water, sanitation, and cooking fuel. Tobacco use was assessed by self-reported questions and categorized into never smokers, noncurrent smokers, current nondaily smokers, and current daily smokers. We used the average number of daily servings consumed in a typical day to assess fruit and vegetable consumption. Five or more servings of fruits and/or vegetables were defined as sufficient daily intake (equivalent to at least 400 g/day). Fewer than 5 servings were categorized as insufficient. We generated a categorical indicator of physical activity (low, moderate, and high levels) from the self-reported total time spent on physical activity, the number of days, and the intensity of physical activity (21). The study asked fuel types for domestic cooking and usage of ventilation to assess indoor air pollution. It categorized them as clean fuels (electricity and natural gas) and unclean fuels (coal, wood, dung, and agricultural residues).
Statistical Methods
We used descriptive statistics to describe the sociodemographic characteristics and selected covariates for respondents in the 6 countries, including observations and weighted proportions for categorical variables. We generated country-specific frequency histograms to describe the 3-year averaged PM2.5 concentration distribution. A 3-level hierarchical logistic model was used to evaluate the association between long-term 3-year PM2.5 exposure and frailty, considering that individuals were nested within provinces, which were nested within countries. Covariates of interest at the individual level (Level 1) included age, gender, education, tobacco use, vegetable and fruit intake, and physical activity. All models were stratified by urban/rural setting. To avoid small regression coefficients, we set 10 μg/m3 instead of 1 μg/m3 as the minimum concentration unit. We also performed a stratified analysis to compare whether the associations varied by country, age group, or gender to identify the potential effect modifiers. We evaluated the dose–response association between PM2.5 concentrations and frailty by modeling the PM2.5 concentrations using restricted cubic splines with knots at the PM2.5 concentration quartiles. We generated the plots separately for urban and rural areas.
We also conducted sensitivity tests to assess the robustness of the analyses to different PM2.5 exposure duration and different scales of frailty as the outcome variable. First, in addition to the 3-year PM2.5 exposure, we also tested for different exposure durations, including 1-year, 2-year, 4-year, and 5-year PM2.5 exposures. Second, we repeated all the analyses and used the frailty index as a continuous variable in the model. All analyses were performed using Stata v16.0 (StataCorp, College Station, TX). We used the p value <.05 to determine statistical significance.
Role of the Funding Source
The funder had no role in the study design; collection, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication.
Results
A total of 38 670 individuals aged 50 and older participated in SAGE Wave 1, among which 4 532 individuals who could not complete or partially completed an interview or with missing sociodemographic variables were excluded from the analyses. China had the largest sample (N = 13 070), and Mexico (N = 2 249) had the smallest.
The demographic and socioeconomic characteristics of the respondents differed widely across the 6 countries (Table 1). The proportion of women was higher in China, Mexico, Russia, and South Africa and the 50–59 age group category was the largest proportion of respondents in all countries. More than two thirds of Indians resided in rural areas. Most older Mexicans, Russians, and South Africans lived in urban areas. Compared to other SAGE countries, participants in Ghana and India had lower educational levels, with 64.2% and 60.8%, respectively, of the older population having less than primary education. Men were more likely than women to use tobacco in all 6 countries. The prevalence of inadequate fruit and vegetable intake was high in all countries, particularly among India’s older population. The prevalence of low physical activity level was highest in South Africa at 59.2%.
Table 1.
Demographic Characteristics Among SAGE Respondents Aged 50 Years and Older (Weighted), by Country
| China (N = 13 070) | Ghana (N = 4 271) | India (N = 6 415) | Mexico (N = 2 249) | Russian Federation (N = 3 751) | South Africa (N = 3 796) | |
|---|---|---|---|---|---|---|
| Weighted Percentages | ||||||
| Age group | ||||||
| 50–59 | 45.1 | 40 | 49.2 | 48.5 | 44.9 | 50 |
| 60–69 | 31.9 | 27.5 | 31 | 25.7 | 26.8 | 30.7 |
| 70–79 | 18.5 | 22.9 | 15.7 | 17.6 | 21 | 13.8 |
| 80+ | 4.5 | 9.6 | 4.2 | 8.2 | 7.3 | 5.5 |
| Gender | ||||||
| Men | 49.8 | 52.6 | 51.5 | 47 | 42.1 | 44 |
| Women | 50.2 | 47.4 | 48.5 | 53 | 57.9 | 56 |
| Residence | ||||||
| Urban | 47.3 | 40.9 | 29.0 | 79 | 70.7 | 64.8 |
| Rural | 52.7 | 59.1 | 71.0 | 21 | 29.3 | 35.2 |
| Education | ||||||
| Less than primary | 42 | 64.2 | 60.8 | 55.2 | 1.5 | 49.2 |
| Primary school completed | 21 | 11 | 14.9 | 24.1 | 5.3 | 22.3 |
| Secondary school completed | 19.8 | 4 | 10.3 | 10 | 17.6 | 14.3 |
| High school completed | 12.6 | 17.2 | 8.7 | 2.4 | 54.6 | 8.5 |
| College completed and above | 4.5 | 3.6 | 5.2 | 8.2 | 21 | 5.8 |
| Wealth quintile | ||||||
| Q1 （lowest） | 16.2 | 18.3 | 18 | 15 | 13.2 | 20.8 |
| Q2 | 18.1 | 19.1 | 19.5 | 24.8 | 16.9 | 19.9 |
| Q3 | 20.4 | 20.4 | 18.6 | 16.7 | 19.2 | 18.2 |
| Q4 | 23.4 | 20.7 | 19.8 | 16.7 | 22.3 | 19.7 |
| Q5 (highest) | 21.8 | 21.5 | 24.1 | 26.8 | 28.4 | 21.4 |
| Fruit and vegetable intake | ||||||
| Sufficient | 64.4 | 31.1 | 9.4 | 18.6 | 19.1 | 31.3 |
| Insufficient | 35.6 | 68.9 | 90.6 | 81.4 | 80.9 | 68.7 |
| Tobacco use | ||||||
| Never smoker | 64.1 | 75.4 | 45.4 | 60.5 | 65 | 67.6 |
| Noncurrent smokers | 6.6 | 14.3 | 4.6 | 19.2 | 13.4 | 9.6 |
| Current nondaily smokers | 2.5 | 2.6 | 2.9 | 7 | 2.1 | 3.4 |
| Current daily smokers | 26.9 | 7.7 | 47.1 | 13.3 | 19.5 | 19.4 |
| Physical activity | ||||||
| High level | 44.6 | 62 | 52.7 | 40 | 62.6 | 28.5 |
| Moderate level | 27.4 | 12.5 | 23 | 22.5 | 15.7 | 12.3 |
| Low level | 28.1 | 25.5 | 24.3 | 37.5 | 21.7 | 59.2 |
The 3-year averaged PM2.5 concentration in the 6 countries was 22.6 μg/m3. China and India had the highest average PM2.5 concentrations (32.9 and 31.0 μg/m3, respectively), while South Africa had the lowest level of PM2.5 (6.0 μg/m3; Figure 1).
Figure 1.
The 3-year averaged PM2.5 concentration across 6 middle-income countries. *Unique value of PM2.5 concentration.
The prevalence of frailty for each country and by age group, gender, and residential areas is displayed in Table 2. The prevalence of frailty was the highest in India, while China had the lowest prevalence at 14.5%. In all 6 countries, frailty prevalence was higher in older age groups, men, and rural residents.
Table 2.
The Prevalence of Frailty* Among Older Men and Women, by Country
| China | India | Mexico | Russian Federation | South Africa | Ghana | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Weighted Percent（%） | Number | Weighted Percent（%） | Number | Weighted Percent（%） | Number | Weighted Percent（%） | Number | Weighted Percent（%） | Number | Weighted Percent（%） | Number | |
| Total | 14.5 | 13 028 | 51.6 | 6 377 | 34.3 | 2 257 | 39.9 | 3 751 | 41.3 | 3 780 | 39.2 | 4 266 |
| Age group | ||||||||||||
| 50–59 | 7.8 | 5 687 | 40.2 | 2 919 | 24.5 | 430 | 19.9 | 1 427 | 35.8 | 1 681 | 22.0 | 1 686 |
| 60–69 | 15.0 | 3 900 | 55.3 | 2 187 | 34.6 | 924 | 42.9 | 1 026 | 41.5 | 1 221 | 37.2 | 1 194 |
| 70–79 | 24.5 | 2 733 | 70.4 | 1 017 | 49.2 | 600 | 63.4 | 971 | 49.2 | 650 | 56.8 | 973 |
| 80+ | 41.2 | 750 | 87.0 | 292 | 59.2 | 302 | 86.6 | 327 | 67.8 | 245 | 74.5 | 418 |
| Gender | ||||||||||||
| Men | 11.9 | 6 124 | 41.2 | 3 250 | 29.5 | 894 | 35.7 | 1 334 | 36.2 | 1 618 | 34.1 | 2 237 |
| Women | 17.4 | 6 946 | 62.6 | 3 165 | 38.5 | 1 355 | 43.2 | 2 417 | 45.0 | 2 179 | 44.8 | 2 034 |
| Residence | ||||||||||||
| Urban | 13.7 | 6 362 | 47.1 | 1 657 | 31.9 | 1 657 | 38.1 | 2 894 | 40.7 | 2 532 | 38.6 | 1 741 |
| Rural | 15.6 | 6 709 | 53.4 | 4 758 | 43.2 | 604 | 44.3 | 862 | 42.0 | 1 264 | 39.6 | 2 530 |
*Frailty was defined as the accumulation of deficits using the frailty index approach. Index values of 0 to less than 0.2 were categorized as not frail, index values of 0.2–1.0 were categorized as frail.
Table 3 presents the associations between the prevalence of frailty and ambient PM2.5 exposure and the separate stratified analyses. Overall, for rural residence, each 10 μg/m3 increase in ambient PM2.5 was associated with a 29.7% increase in the odds of frailty (OR = 1.297, 95% CI: 1.211–1.388) after adjusting for various potential confounding factors including age, sex, education, wealth index, tobacco use, physical activity levels, temperature, and humidity. We did not observe significant associations in urban areas. In China, though, higher ambient PM2.5 was associated with lower odds of frailty in urban areas. But in South Africa, there was a significant positive association between ambient PM2.5 and frailty in both urban and rural areas. The gender-stratified association between ambient PM2.5 and frailty showed that the association seems to be slightly stronger in men (OR = 1.314, 95% CI: 1.182–1.460) than in women (OR = 1.207, 95% CI: 1.071–1.359) in rural areas. In spline regression models (Figure 2), the dose–response relationship was progressive over the range of ambient PM2.5 concentrations in rural areas.
Table 3.
Results From Stratified Analyses Examining the Association Between Ambient PM2.5 Exposure and Frailty
| PM2.5 (per 10 μg/m3 increase) | Urban | Rural | |||
|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | ||
| Overall | 1.030 | 0.717–1.480 | 1.297** | 1.211–1.388 | |
| Country | |||||
| China | 0.835* | 0.705–0.991 | 0.926 | 0.761–1.126 | |
| Ghana | 1.358 | 0.109–16.943 | 2.337 | 0.457–11.958 | |
| India | 1.069 | 0.845–1.353 | 0.988 | 0.906–1.078 | |
| Mexico | 1.693 | 0.483–5.931 | 5.195* | 1.056–25.555 | |
| Russian Federation | 0.309 | 0.018–5.442 | 0.180 | 0.001–54.057 | |
| South Africa | 10.473** | 3.616–30.332 | 4.780** | 1.625–14.060 | |
| Age group | |||||
| 50–59 | 1.101 | 0.644–1.882 | 1.231** | 1.145–1.325 | |
| 60–69 | 0.925 | 0.632–1.354 | 1.335** | 1.111–1.603 | |
| 70–79 | 0.767** | 0.702–0.838 | 1.016 | 0.841–1.227 | |
| 80+ | 0.868 | 0.752–1.001 | 0.885 | 0.572–1.368 | |
| Gender | |||||
| Men | 0.947 | 0.711–1.260 | 1.314** | 1.182–1.460 | |
| Women | 0.925 | 0.608–1.406 | 1.207** | 1.071–1.359 |
Notes: OR = odds ratio; CI = confidence interval. Odds ratios are adjusted for age, education, gender, wealth index, fruit and vegetable intake, tobacco use, physical activity level, cooking fuel use, temperature, and humidity.
*p < .05, **p < .01.
Figure 2.
The concentration–response curves for PM2.5 on frailty in urban and rural areas of 6 low- and middle-income countries. Note: Odds ratios (95% confidence intervals) of frailty according to PM2.5 concentrations based on restricted cubic splines with knots at the quartiles. The reference value is set as the minimum value. Odds ratios are adjusted for age, education, gender, wealth index, fruit and vegetable intake, tobacco use, physical activity levels, cooking fuel use, temperature, and humidity. Lines represent the odds ratio (thick line) and 95% confidence interval (dashed lines).
The sensitivity analyses suggested that the length of exposure had little effect on the association between PM2.5 and frailty in rural areas. Similarly, the association remains unchanged significantly after including the frailty index as a continuous variable in the model (Supplementary Tables 1 and 2).
Discussion
In a large sample of community-based adults aged 50 years and older from 6 MICs, we found strong evidence that long-term PM2.5 exposure was associated with frailty in rural areas. The association was robust, remaining significant when using the average concentrations of 1-year, 2-year, 4-year, and 5-year PM2.5 exposure before the survey after adjusting for demographic characteristics, behavioral risk factors, and indoor air pollution. To our best knowledge, this is the first study to investigate the association between ambient PM2.5 exposure and frailty as measured using a frailty index among community-dwelling older adults in MICs.
Numerous epidemiological studies have demonstrated a positive correlation between exposure to ambient PM2.5 and risk of cardiovascular disease (22,23), the incidence of acute coronary events, and cerebrovascular events (10,24). Some studies have also highlighted an association between exposure to ambient PM2.5 and depression in low-income countries and MICs (8). Very few studies have investigated the effect of long-term exposure to ambient PM2.5 on frailty. In a nationwide analysis of 28 million individuals 55 years and older across 3 034 counties in the United States, higher levels of PM2.5 air pollution were associated with lower population-based probabilities of exceptional aging, defined as reaching age 85 years (25). Another study found increased odds of developing frailty as measured using a frailty index associated with exposure to PM2.5 among postmyocardial infarction patients (9). Our study has for the first time described the association between long-term exposure to ambient PM2.5 and frailty among the general population aged 50 and older in middle-income settings. We also found an approximately linear dose–response relationship in rural areas in each country.
Air pollution, including long-term exposure to ambient PM2.5, induces chronic systemic oxidative stress, inflammation, hormonal changes, and genetic and epigenetic modifications (26,27), all of which can cause damage to cellular and molecular structures. This damage may promote cumulative decline in multiple physiological systems like the endocrine, immune, and skeletal muscle. Abnormal results in 3 or more systems are a significant predictor of frailty (28). This suggested the onset of frailty when physiological decline reaches an aggregate critical mass. However, while markers of inflammation related to air pollution were not predictive of the onset of frailty in one study (3), underlying environmental factors like long-term exposure to ambient PM2.5 in combination with genetic factors may play an essential role in this process. In this study, physiological decline and genetic factors might be underlying mechanisms contributing to the observed association between long-term exposure to ambient PM2.5 and frailty.
The country-stratified analyses showed that the association between ambient PM2.5 and frailty is quite different in urban China and South Africa; the reason for the difference is poorly understood. Still, there are significant differences in PM2.5 exposure and sample size between the 2 countries. That is why we further used pooled data to analyze the association between PM2.5 and frailty after adjusting for the impact at the national level. The gender-stratified analysis also showed that the association seems to be slightly stronger in men than in women in rural areas. This result is consistent with a Danish cohort study that showed higher hazard ratios for all-cause and cardiovascular disease mortality in men than in women for PM2.5 and PM10 (29).
Our findings suggested that PM2.5 exposure was associated with an increased risk of frailty in rural-dwelling older adults, but no consistent associations were observed in urban areas. The differences between urban and rural areas are unknown, especially with the widespread media coverage of urban pollution in Beijing and New Delhi, for example, but could be related to the type of employment. Public health and policy measures would be crucial in preventing air pollution. Differences in how the policy is implemented and monitored in urban and rural areas may contribute to the differences. Health care access and supply often differ between urban and rural areas and may play a role in compensating for health loss caused by environmental pollution. Even though association estimates were generally inconsistent in urban areas, we noted a more robust association among males, older respondents, and those with lower education levels. Our findings warrant further investigation of location-specific mechanisms and other community or individual characteristics that may play a role in the onset of frailty.
Our study was based on a large population-based sample of respondents with relatively high response rates across the 6 MICs. Furthermore, we included vital risk factors that may have affected the results, including socioeconomic status, behavioral risk factors, indoor air pollution, and other factors, for potential confounding. The inclusion should help to improve the study’s ability to detect any real associations. There are also limitations to acknowledge. This study can only state associations between long-term PM2.5 and frailty but cannot infer causality, in part because of the cross-sectional data used in this study SAGE Wave 1. The use of remote sensing data in estimating PM2.5 in grid cell of 100 km2 in our study resulted in relatively small exposure contrast, contributing to measurement errors in PM2.5 exposure. However, this method is widely applied to estimate the global burden of ambient PM2.5 (20,30). A study in China during 2013–2015 also reported a high correlation between ground-based PM2.5 concentrations and estimates from monitoring stations (23). In addition, due to the limited data, we only used the annual average PM2.5 concentration values as the exposure variable. The use of annual concentration may result in less accurate results, especially in areas with high variable PM2.5 concentrations over the year. However, as our health outcome, frailty index, reflects a more stable, chronic long-term situation collected at a one time point, we argue that higher resolution PM2.5 concentrations and temperature data might not provide additional information to address our research question. Finally, we also did not consider the effect of population migration on PM2.5 exposure assessment. The effect of population movement on effect estimation is more complex; the population in the exposed area can move out or in or both. If the population only moves out from the exposed area, the effect estimate is underestimated to a greater extent as the migration rate increases.
In summary, our study suggests that long-term exposure to ambient PM2.5 is a significant risk factor of frailty in the rural-dwelling population aged 50 years and older in 6 MICs. More efforts are needed to protect older adults from ambient outdoor and indoor air pollution, especially in rural areas. In addition, more studies are needed with a particular focus on whether or how environmental pollutants affect frailty in older adult populations.
Supplementary Material
Acknowledgments
We accomplished the study within the context of the Swedish National Graduate School for Competitive Science on Ageing and Health (SWEAH) funded by the Swedish Research Council.
Contributor Information
Yanfei F Guo, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, China; School of Public Health and Community Medicine, Institution of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Nawi Ng, School of Public Health and Community Medicine, Institution of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Epidemiology and Global Health, Faculty of Medicine, Umeå University, Umeå, Sweden.
Paul Kowal, International Health Transitions, Canberra, Australian Capital Territory, Australia; University of Newcastle, School of Medicine and Public Health, Newcastle, New South Wales, Australia.
Hualiang Lin, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Ye Ruan, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, China.
Yan Shi, Shanghai Municipal Centre for Disease Control and Prevention, Shanghai, China.
Fan Wu, Shanghai Medical College, Fudan University, Shanghai, China.
Funding
This work was supported by the World Health Organization and the US National Institute on Aging through Interagency Agreements (OGHA 04034785, YA1323-08-CN-0020, Y1-AG-1005-01) and a research grant (R01-AG034479); the Shanghai New Three-year Action Plan for Public Health (grant No. GWV-10.1-XK16). The Network for International Longitudinal Studies on Ageing, funded by the Swedish Forte Network grant (Dnr: 2015-01499), supported N.N.’s contribution in this article. It was also supported by Shanghai Municipal Health Commission, Shanghai, China (201840118).
Conflict of Interest
None declared.
Author Contributions
All authors contributed to the study concept and design, acquisition, analysis, or interpretation of data. Y.F.G. and N.N. did the statistical analyses. Y.F.G. conducted the literature search and wrote the first draft of the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version. F.W. obtained the funding.
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