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Association of Indoor and Outdoor Air Pollution With Hand-Grip Strength Among Adults in Six Low- and Middle-Income Countries

Association of Indoor and Outdoor Air Pollution With Hand-Grip Strength Among Adults in Six Low-... Abstract Background Air pollution has been associated with various health outcomes. Its effect on hand-grip strength, a measurement of the construct of muscle strength and health status, remains largely unknown. Methods We used the survey data from 31,209 adults ≥ 50 years of age within Wave 1 of the Study on Global AGEing and Adult Health in six low- and middle-income countries. The outdoor concentration of fine particulate matter pollution (PM2.5) was estimated using satellite data. Domestic fuel type and ventilation were used as indicators of indoor air pollution. We used multilevel linear regression models to examine the association between indoor and outdoor air pollution and hand-grip strength, as well as the potential effect modifiers. Results We found inverse associations between both indoor and outdoor air pollution and hand-grip strength. Each 10 μg/m3 increase in 3 years’ averaged concentrations of outdoor PM2.5 corresponded to 0.70 kg (95% CI: −1.26, −0.14) lower hand-grip strength; and compared with electricity/liquid/gas fuel users, those using solid fuels had lower hand-grip strength (β = −1.25, 95% CI: −1.74, −0.75). However, we did not observe a statistically significant association between ventilation and hand-grip strength. We further observed that urban residents and those having a higher education level had a higher association between ambient PM2.5 and hand-grip strength, and men, young participants, smokers, rural participants, and those with lower household income had higher associations between indoor air pollution and hand-grip strength. Conclusion This study suggests that both indoor and outdoor air pollution might be important risk factors of poorer health and functional status as indicated by hand-grip strength. Air pollution, Hand-grip strength, Low- and middle-income countries, Effect modification Both indoor and outdoor air pollution have been known to be associated with various health outcomes, including nervous system health (1,2). Muscle power, including hand-grip strength, is the product of force generated and speed of movement, and has been viewed as the ability of the neuromuscular system to produce the greatest and fastest force (3,4). Hand-grip strength is an essential component of work and daily living, and a measurement of the construct of muscle strength and health status (5,6). Several studies have reported that exposure to occupational substances is associated with lower hand-grip strength (7). For example, occupational exposure to organophosphate-induced delayed polyneuropathy has been associated with lower hand-grip and pinch strength (8,9). It is reasonable to hypothesize that exposures to both outdoor and indoor air pollution are also associated with hand-grip strength; however, there has been no study examining the effects of air pollution on hand-grip strength in the literature. This linkage has also been supported by several biological mechanisms. Some pollutants may lead to inhibition of cholinesterase enzymes resulting in reductions in acetylcholine in the brain (10), which could consequently lead to poor muscle function (11). Some chemical species, such as manganese, could reduce dopamine production in the brain, resulting in Parkinson-like symptoms (12). In sum, exposures to indoor and outdoor air pollution may be an important risk factor of lower hand-grip strength; however, evidence on this association is limited. Along with the rapid economic development in low- and middle-income countries has come an increase in both indoor and outdoor air pollutants, which has in turn created new, important public health concerns (13). The main objectives of this study were to: (i) examine whether exposure to ambient particulate matter with an aerodynamic diameter of equal or less than 2.5 μm (PM2.5) is associated with lower hand-grip strength, (ii) explore whether exposure to indoor air pollution (using domestic fuel type and ventilation as indicators) is associated with lower hand-grip strength, and (iii) investigate the potential effect modifiers of the associations in order to find the vulnerable subpopulations, such as sex, age, smoking, urbanity, education, and household income. Methods Study Population We employed the baseline survey (Wave 1) data from the Study on Global AGEing and Adult Health (SAGE) conducted during 2007–2010. SAGE is an ongoing study with representative participants from six low- and middle-income countries: China, Ghana, India, Mexico, Russia, and South Africa (Figure 1). The details of the survey have been described previously (14,15). In brief, it was implemented through a face-to-face household interview using a stratified multistage random cluster sampling design. Wave 1 was a cross-sectional survey that collected information on adults aged 50 years and older and a small group of younger participants between the ages of 18 and 49 years. For this study, we restricted our sample to those aged 50 years and older. Figure 1. Open in new tabDownload slide The geographic location of the study communities in the six countries. Figure 1. Open in new tabDownload slide The geographic location of the study communities in the six countries. Measurement of Hand-Grip Strength Hand-grip strength was measured in both hands using a Smedley’s hand dynamometer (16). Participants who had any previous surgery on their arms, hands, or wrists during the last 3 months, or who had arthritis or pain in their hands or wrists, were excluded from the measurement. During the measurement, the study participants were seated in the upright position with their arm along their body; the arm was bent at 90° at the elbow with the forearm and wrist in the neutral position. Hand-grip strength (in kilograms, kg) was measured two times in each hand with brief pauses between each measurement. The highest of the four measurements was considered the maximum hand-grip strength and was used in the subsequent analysis. Air Pollution Assessment For this study, the domestic fuel type and ventilation apparatus for cooking were used as indicators of indoor air pollution. Two fuel types were mainly used for domestic cooking: solid fuels, such as coal, wood, dung, and agricultural residues; and electricity, liquid and gas fuels (including liquefied petroleum gas and natural gas). Respondents were also asked whether there was an indoor ventilation apparatus in the area where cooking was done (chimney, extraction hood, fan, or none) (17). Ambient PM2.5 was used as the indicator for outdoor air pollution, which was estimated using a global satellite-derived PM2.5 estimation (18). To measure the light extinction due to aerosol, aerosol optical depth data were retrieved from the Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging Spectroradiometer instruments from the Terra satellite. By combining aerosol optical depth data with vertical profiles of aerosol data from a chemical transport model (19), we can estimate ground-level PM2.5 concentrations. The community locations in SAGE were geo-coded and linked with the estimated PM2.5 concentrations. We used the average PM2.5 for the 3 years immediately preceding the survey as the independent variable in the main models. As one validation, we compared the monitored PM2.5 concentrations (measured by beta ray attenuation method (20)) with the satellite estimated PM2.5 concentrations in China and observed that the estimated PM2.5 concentrations closely represented the actual monitoring measurements (with an R square of 0.80, as shown in Supplementary Figure 1). Covariates A series of covariates were considered in this study, including individual-level variables and demographic, socioeconomic, occupational, and lifestyle factors. Individual-level variables included age, sex, body mass index (BMI), marital status, smoking status and amount, alcohol consumption, physical activity, education, and household income. Marital status was classified into two broad groups: married and unmarried. Those who were either never married, separated, divorced, or widowed were classified as unmarried. Occupations were classified as air pollution related occupations (including mineral, construction, cleaning, renovation, mechanic-related work) (21), or not related to occupational air pollution exposure (such as administrative, office work, service, academic, sales, fishery, unemployed, etc.). Lifetime tobacco consumption was assessed in terms of smoking status (smoker or not), and the average amount of tobacco products or cigarette equivalents consumed per day. The Global Physical Activity Questionnaire was used to measure the intensity, duration, and frequency of physical activity (22). Three levels of physical activity (low, moderate, and high levels) were classified based on the participants’ responses to the questions about moderate or vigorous physical activities during work, transport activities to and from places, and recreational/leisure time activities. Each type of activity was categorized into low, moderate, or high levels, according to the time spent on each activity and its total energy requirement in metabolic equivalents. At the country level, we considered a few covariates. Gross domestic product per capita was obtained from the Central Intelligence Agency’s World Factbook. Percentage of population living in urban areas and per capita health care expenditure were retrieved from the World Bank’s World Development Indicators. Statistical Analysis The hand-grip strength of participants in the same communities may be dependent on each other, violating the independence assumption of regression models. We therefore applied a three-level linear mixed regression model with participants as the first-level units, the community as the second-level unit, and the country as the third-level unit (23,24). We selected covariates in the final models based on two criteria: (i) variables are known or hypothesized to be risk factors for lower hand-grip strength and (ii) univariate model analyses showed an association with the hand-grip strength (25). Variables that were associated with the outcome were included in the final multivariate models (Supplementary Table 1). Other important covariates, such as age, sex, and smoking status were also included in the models, even if they were not statistically significant in the univariate models. Therefore, Model 1 for both PM2.5 and indoor air pollution exposures included age, sex, BMI, education, marital status, occupational air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey. Model 2 for PM2.5 further included fuel type and ventilation, and Model 2 for fuel type and ventilation further included PM2.5. The estimated associations were expressed as absolute differences in hand-grip strength associated with both indoor and outdoor air pollution. For outdoor air pollution, we calculated the associations for per 10 μg/m3 increase in ambient PM2.5 concentrations. For indoor air pollution, we used “electricity/liquid/gas fuel type” and “without ventilation apparatus” as the reference, respectively, and examined the associations with solid fuel type and using a chimney or hood as the ventilation. We also constructed one composite indicator to combine these two variables: (i) electricity/liquid/gas fuel + ventilation (as reference), (ii) electricity/liquid/gas fuel + no ventilation, (iii) solid fuel + ventilation, and (iv) solid fuel + no ventilation. To examine potential effect modifiers, stratified analyses were performed using the following categorical variables: sex (men and women), age group (younger than 65 years vs 65 years and older), smoking status (nonsmokers vs ever-smokers), urbanity (urban vs rural), education (high vs low), and household income (high vs low). The statistical difference of the associations between the subgroups was examined by including an interactive term of air pollution and the potential effect modifier in the model (26). A series of sensitivity analyses was conducted. First, additional country-level covariates were incorporated into the models to control for potential confounding at the country level, specifically, we considered the gross domestic product per capita, the percentage of urban population, health care expenditure per capita, and the Gini coefficient (27). Second, we included the city variable in the second level to control for the unmeasured city-level characteristics. Third, indicators were assessed for different lengths of exposure by using average PM2.5 concentrations for 1, 2, 4, and 5 years before the survey. Fourth, we also used the annual PM2.5 concentrations estimated from the spatial resolution of 1 * 1 km as the exposure, which were derived from the annual PM2.5 concentrations estimated at a 10 ∗ 10 km resolution. Combined with local meteorological parameters and land use information, the estimated PM2.5 concentrations were then down-scaled into a 1 ∗ 1 km resolution (28). For the missing values of hand-grip strength and some important covariates of interest (such as BMI, household income, education level), we compared the distributions between the participants with and without missing data. We also performed an imputation to replace the missing values and conducted the regression model. To exclude the impacts of height, we also used the ratio between hand-grip strength and height as the dependent variable in the analysis. All the analyses were conducted using R version 3.2.2. In all analyses, a p-value <.05 was considered statistically significant. Results A total of 40,583 participants aged 50 years and older were initially contacted, among which, 36,742 agreed to participate in this survey, resulting in a response rate of 90.5%. Among them, 5,484 participants did not have a valid measurement of hand-grip strength, and the other 49 had missing values for age, sex, or other important covariates. The remaining 31,209 participants were included in this analysis. Though statistically significant, there were generally comparable characteristics between the participants included in the analysis and those excluded (Supplementary Table 2), including similar exposure to PM2.5 (23.3 and 23.6 μg/m3), and similar BMI (24.6 and 26.1 kg/m2), indicating a representative sample of the participants included in this analysis. Table 1 summarizes the sample sizes and mean ages, plus PM2.5 characteristics by country. Among the 31,209 participants from the six countries, the mean age was 63 years. The 3-year mean PM2.5 concentration in the six countries was 23.33 μg/m3. South Africa had the lowest level of PM2.5 with an annual concentration of 6.00 μg/m3; China and India had the highest PM2.5 concentrations at 32.32 and 31.08 μg/m3, respectively. Table 1. Description of Population and Air Pollution Characteristics, by Country, SAGE Wave 1 (2007–2010) Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Note: SD = standard deviation; min = minimum; max = maximum. *The 3 years’ average concentration of PM2.5 was used. Open in new tab Table 1. Description of Population and Air Pollution Characteristics, by Country, SAGE Wave 1 (2007–2010) Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Note: SD = standard deviation; min = minimum; max = maximum. *The 3 years’ average concentration of PM2.5 was used. Open in new tab Supplementary Table 3 provides more details about the general characteristics of the participants, stratified by sex. Among the 31,209 participants, 16,649 (53.35%) were women and 14,560 (46.65%) were men. The mean hand-grip strength was 29.11 kg, and relatively higher among men than women (34.50 vs 24.39 kg). The mean age was similar between men and women. Women had a higher BMI than men (25.34 kg/m2 for women and 23.68 kg/m2 for men). Men were more likely to have occupational air pollution exposure, to be married, to live in rural areas, have higher household income, and to report smoking or drinking. A higher percentage of men used solid fuels, but a higher proportion used ventilation. Table 2 shows the associations between exposure to PM2.5, indoor air pollution (fuel type and ventilation), and hand-grip strength in the univariate and multivariate models. Comparable estimates were observed for the average concentration of PM2.5 over different years. We presented and conducted the subsequent analyses based on the 3 years’ average concentration. In the univariate regression model, each 10 μg/m3 increase in the 3 years’ average concentration of ambient PM2.5 was associated with a 0.70 kg (95% CI: −1.26, −0.14) lower hand-grip strength. Similar estimates were obtained in the multivariate models controlling for various factors, with (Model 2) or without (Model 1) fuel type and ventilation adjusted. Compared with electricity/liquid/gas fuel users, those using solid fuels indoors had relatively lower levels of hand-grip strength (β = −1.25, 95% CI: −1.74, −0.75), and the adjusted estimates were −0.90 (95% CI: −1.39, −0.40) without ambient PM2.5 in the multivariate model (Model 1) and −0.86 (95% CI: −1.35, −0.37) in the multivariate model including ambient PM2.5 (Model 2). We did not find statistically an association with ventilation in either the univariate or multivariate models. The results of the composite indicator (Supplementary Table 4) showed that, compared with the reference group (electricity/liquid/gas and ventilation), there was a negative association of the combination of solid fuel and adequate ventilation (β = −0.86, 95% CI: −1.33, −0.35), and the combination of solid fuel and no ventilation (β = −0.77, 95% CI: −1.31, −0.24). However, we did not find any association in the electricity/liquid/gas fuel and no ventilation group. Table 2. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution, SAGE Wave 1 (2007–2010) Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Note: CI = confidence interval. *Model 1: adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey. †Model 2 further adjusted for fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type and ventilation). Open in new tab Table 2. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution, SAGE Wave 1 (2007–2010) Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Note: CI = confidence interval. *Model 1: adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey. †Model 2 further adjusted for fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type and ventilation). Open in new tab Table 3 illustrates the associations in the stratified analyses. For the associations with outdoor PM2.5, participants living in urban areas and having a higher education level had higher associations, each 10 μg/m3 increase in ambient PM2.5 corresponded to 2.27 kg (95% CI: −3.47, −1.07) and 3.15 kg (95% CI: −4.55, −1.76) lower hand-grip strength, respectively; while the corresponding estimates were −0.46 kg (95% CI: −1.00, 0.08) and −0.71 kg (95% CI: −1.24, −0.18) among the rural residents and those with a lower educational level. We did not find effect modifications by sex, age group, smoking status, and household income on the association. Table 3. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution: Results From the Stratified Analyses Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Note: CI = confidence interval. p value is for the difference among the strata. For fuel type, we reported the effect of solid fuels on hand-grip strength compared with electricity, liquid and gas fuels. Adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey, as well as fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type). *The 3 years’ average concentration of PM2.5 was used. Open in new tab Table 3. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution: Results From the Stratified Analyses Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Note: CI = confidence interval. p value is for the difference among the strata. For fuel type, we reported the effect of solid fuels on hand-grip strength compared with electricity, liquid and gas fuels. Adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey, as well as fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type). *The 3 years’ average concentration of PM2.5 was used. Open in new tab For the associations with solid fuel usage, we found statistically significant modifications of age, urbanity, and household income (Table 3). For example, compared with electricity, liquid and gas fuels usage, solid fuels usage was associated with a lower hand-grip strength of 1.21 kg (95% CI: −1.91, −0.52) among participants younger than 65 years, and 0.76 kg (95% CI: −1.56, 0.05) among those aged 65 years and older (though the difference between younger and older participants was only marginally significant, p = .05). For rural residents the lower hand-grip strength was 1.24 kg (95% CI: −1.89, −0.58) and 0.60 kg (95% CI: −1.36, 0.15) among urban residents. Those with a lower household income had a lower hand-grip strength of 1.14 kg (95% CI: −1.83, −0.46) while those of higher household income had a lower hand-grip strength of 0.30 kg (95% CI: −1.01, 0.40). The differences in the estimated associations were not statistically significant by sex, smoking status, and education levels. The sensitivity analyses using different years’ concentrations as the exposure variable produced consistent estimates (Table 1 and Supplementary Table 5). For example, each 10 μg/m3 increase in PM2.5 concentration was associated with lower hand-grip strength (β = −0.68, 95% CI: −1.24, −0.11 for 1-year PM2.5). When country-level covariates were included in the model, each 10 μg/m3 increase in PM2.5 was associated with a 0.75 kg lower hand-grip strength (95% CI: −1.29, −0.21). The models with the city variable in the second level also produced consistent results. For example, each 10 μg/m3 increase in PM2.5 corresponded to a 0.88 kg lower hand-grip strength (95% CI: −1.66, −0.10). The analyses based on the 1 * 1 km estimated PM2.5 also yielded comparable estimates. When replacing the missing values by imputation, we also obtained a consistent association. For example, each 10 μg/m3 increase in PM2.5 was associated with a 0.85 kg lower hand-grip strength (95% CI: −1.40, −0.31) using the complete data set. When using the ratio between hand-grip strength and height as the dependent variable, we also obtained statistically significant associations with both ambient PM2.5 and indoor air pollution. Discussion Our study showed that both indoor air pollution from solid fuel burning and ambient PM2.5 were associated with lower hand-grip strength. However, ventilation was not found to be associated with hand-grip strength. Additionally, our study found that participants living in urban areas and having a higher education level were more susceptible to the impacts of ambient PM2.5. Furthermore, participants younger than 65 years, rural participants, and those with lower household income were more susceptible to the impacts of indoor air pollution resulting from solid fuel usage. The linkage of indoor and outdoor air pollution with hand-grip strength has not been well studied, though occupational exposures to pesticides, solvents, and some heavy metals have been associated with lower hand-grip strength (29), and both indoor and outdoor air pollution share some chemical components with occupational setting exposure. In this sense, the observed associations in this study were biologically plausible. One recent study suggested that air pollution was associated with age-related frailty, which was in line with our findings (30). Air pollution has been associated with nervous system-related health outcomes (27). Hand-grip strength has been closely related to the health of the nervous system, and a decline in hand-grip strength has been suggested to be one predictor of nervous impairment (3). It was possible that some components of the air pollutants could impair the nervous system (31,32), which subsequently affected hand-grip strength. The fine particles were able to pass through the throat and nose, and ultimately enter into the circulatory system and brain, and thus resulted in adverse nervous effects, such as stroke, and neurodegenerative diseases (33). This idea has been supported by both human and animal studies (34,35). For example, epidemiological studies have reported that long-term exposure to ambient PM2.5 was associated with increased risk of stroke (27,36). Additionally, the nerve cells of the brain are particularly sensitive to the impacts of particulate pollution exposure (37). Air pollutants could adversely affect the nervous system directly (after penetrating into the brain) or indirectly (through the inflammatory processes and oxidative stress) (38). For indoor air pollution, though this study observed an association with fuel type, ventilation was not found to be associated with hand-grip strength. Chimneys were mainly used for ventilation in rural areas. It was possible that indoor air pollution was still very high in the presence of chimneys, while in urban areas, electricity/liquid/gas fuels and vent hoods were mainly used. Electricity/liquid/gas fuels were related with relatively higher hand-grip strength compared with solid fuels. It was thus understandable that vent hoods were not related to hand-grip strength. Urbanity and education were found to be important effect modifiers of the association between ambient PM2.5 and hand-grip strength. The higher associations among urban residents and those with a higher education level might be associated with the chemical composition and emission source of the particulate pollution. There might be more hazardous components in the particles of the urban areas (39), while the particulate pollution in the rural areas may be more related to natural sources, and thus potentially be less harmful (40). The other possibility might be that air pollution cannot disperse as quickly in urban areas due to the high buildings (41), the urban residents were thus likely to expose to higher levels of air pollution. The higher exposure level among urban residents may also help explain the results of the higher association between the more highly educated participants and lower hand-grip strength, as among the participants in this study, 43% of urban residents were highly educated, compared to only 14% in rural areas. We did not observe a sex differential association between indoor air pollution and hand-grip strength. This was somewhat surprising given that women were more likely to do most of cooking at home. This result must be interpreted cautiously. One possible reason might be the lack of adjustment for environmental tobacco smoking in the model, resulting in residual confounding. It is also possible that women were more adapted to the indoor air pollution due to their longer exposures, and less vulnerable to the impacts of indoor air pollution exposure. Further studies are needed to test this speculation using the follow-up data from the SAGE Wave 2 study. The higher association of indoor air pollution from solid fuel combustion with hand-grip strength among younger participants (50–64 years) than older adults (≥65 years) might indicate that the younger adults may have mainly participated in the domestic cooking, causing them to have more intensive and longer exposures than those over age of 65 years. It is also possible that the hand-grip strength among the older participants remained relatively more stable than the younger adults and less sensitive to the impacts of indoor air pollution; however, more studies are needed to investigate this possibility. Smokers were found to be more vulnerable to the impacts of indoor air pollution from solid fuel usage. It is possible that smoking could decrease the clearance, and increase the deposition and retention of air pollutants, and thus enhance the impacts of indoor air pollution (42). The higher associations in rural areas could be associated with the higher indoor air pollution (43) and higher rates and consumption of smoking in rural areas (44). This is also one possible reason for the higher association among those with lower household income observed in this study. This study possessed a few strengths. To our knowledge, this is the first effort to simultaneously investigate the associations of both indoor and outdoor air pollution with hand-grip strength. Additionally, this study covered a population with a wide range of different characteristics in the six low- and middle-income countries represented in the SAGE study. More importantly, we collected and controlled for a series of important potential confounders in this analysis, and obtained a robust association between air pollution and hand-grip strength. A few limitations should also be noted. As a cross-sectional study, we cannot establish a causal relationship between air pollution and hand-grip strength. Selection bias was possible, especially due to the exclusion of the subjects with existing illnesses of the arms, hands, or wrists from the analysis. Furthermore, we did not have data on long-term air pollution exposures, though our measure may reflect some degree of long-term air pollution exposures. We used ambient PM2.5 of the centroid of the community as an indicator of outdoor air pollution due to data limitation (we did not have the individual addresses of each participant), as we only had the annual concentration of PM2.5. It was hard for us to conduct an exposure assessment before the survey; we thus used the 3 years’ average concentration before the assessment year as an indicator of exposure. This would introduce non-differential exposure misclassification, and bias the results toward the null (27). However, considering the spatial correlation of annual PM2.5 of the residents within a community, we regard that such a bias would not play an important role in this study. Our sensitivity analysis using exposure assessment from 1 * 1 km resolution showed consistent estimates, indicating that spatial misalignment had little impact on the results if any. Further, domestic fuel type and ventilation were crude proxies for indoor air pollution, and we were unable to collect more information, such as the use of both electricity/liquid/gas and solid fuel types in the same household, duration of cooking, or housing type to refine the indoor air pollution exposure. It is not practical to measure air pollution exposures using personal air monitoring devices for such a large-scale epidemiologic study. Additionally, we cannot exclude the possibility of influence of some potential confounding factors, such as environmental tobacco smoking, meteorological factors, and other air pollutants, which might have resulted in residual confounding. In conclusion, this study adds to the evidence that exposure to ambient PM2.5 and indoor air pollution from domestic solid fuels usage is associated with lower hand-grip strength. Additionally, the study shows that more stringent air pollution control measures should be formulated to protect the public’s health. Funding This work was in part supported by the National Key R&D Program of China (grant no: 2018YFA0606200) and Foundation of Shanghai Municipal Commission of Health and Family Planning (general program: 201640148). Acknowledgments We thank all the participants and investigators in this survey. 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Association of Indoor and Outdoor Air Pollution With Hand-Grip Strength Among Adults in Six Low- and Middle-Income Countries

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1079-5006
eISSN
1758-535X
DOI
10.1093/gerona/glz038
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Abstract

Abstract Background Air pollution has been associated with various health outcomes. Its effect on hand-grip strength, a measurement of the construct of muscle strength and health status, remains largely unknown. Methods We used the survey data from 31,209 adults ≥ 50 years of age within Wave 1 of the Study on Global AGEing and Adult Health in six low- and middle-income countries. The outdoor concentration of fine particulate matter pollution (PM2.5) was estimated using satellite data. Domestic fuel type and ventilation were used as indicators of indoor air pollution. We used multilevel linear regression models to examine the association between indoor and outdoor air pollution and hand-grip strength, as well as the potential effect modifiers. Results We found inverse associations between both indoor and outdoor air pollution and hand-grip strength. Each 10 μg/m3 increase in 3 years’ averaged concentrations of outdoor PM2.5 corresponded to 0.70 kg (95% CI: −1.26, −0.14) lower hand-grip strength; and compared with electricity/liquid/gas fuel users, those using solid fuels had lower hand-grip strength (β = −1.25, 95% CI: −1.74, −0.75). However, we did not observe a statistically significant association between ventilation and hand-grip strength. We further observed that urban residents and those having a higher education level had a higher association between ambient PM2.5 and hand-grip strength, and men, young participants, smokers, rural participants, and those with lower household income had higher associations between indoor air pollution and hand-grip strength. Conclusion This study suggests that both indoor and outdoor air pollution might be important risk factors of poorer health and functional status as indicated by hand-grip strength. Air pollution, Hand-grip strength, Low- and middle-income countries, Effect modification Both indoor and outdoor air pollution have been known to be associated with various health outcomes, including nervous system health (1,2). Muscle power, including hand-grip strength, is the product of force generated and speed of movement, and has been viewed as the ability of the neuromuscular system to produce the greatest and fastest force (3,4). Hand-grip strength is an essential component of work and daily living, and a measurement of the construct of muscle strength and health status (5,6). Several studies have reported that exposure to occupational substances is associated with lower hand-grip strength (7). For example, occupational exposure to organophosphate-induced delayed polyneuropathy has been associated with lower hand-grip and pinch strength (8,9). It is reasonable to hypothesize that exposures to both outdoor and indoor air pollution are also associated with hand-grip strength; however, there has been no study examining the effects of air pollution on hand-grip strength in the literature. This linkage has also been supported by several biological mechanisms. Some pollutants may lead to inhibition of cholinesterase enzymes resulting in reductions in acetylcholine in the brain (10), which could consequently lead to poor muscle function (11). Some chemical species, such as manganese, could reduce dopamine production in the brain, resulting in Parkinson-like symptoms (12). In sum, exposures to indoor and outdoor air pollution may be an important risk factor of lower hand-grip strength; however, evidence on this association is limited. Along with the rapid economic development in low- and middle-income countries has come an increase in both indoor and outdoor air pollutants, which has in turn created new, important public health concerns (13). The main objectives of this study were to: (i) examine whether exposure to ambient particulate matter with an aerodynamic diameter of equal or less than 2.5 μm (PM2.5) is associated with lower hand-grip strength, (ii) explore whether exposure to indoor air pollution (using domestic fuel type and ventilation as indicators) is associated with lower hand-grip strength, and (iii) investigate the potential effect modifiers of the associations in order to find the vulnerable subpopulations, such as sex, age, smoking, urbanity, education, and household income. Methods Study Population We employed the baseline survey (Wave 1) data from the Study on Global AGEing and Adult Health (SAGE) conducted during 2007–2010. SAGE is an ongoing study with representative participants from six low- and middle-income countries: China, Ghana, India, Mexico, Russia, and South Africa (Figure 1). The details of the survey have been described previously (14,15). In brief, it was implemented through a face-to-face household interview using a stratified multistage random cluster sampling design. Wave 1 was a cross-sectional survey that collected information on adults aged 50 years and older and a small group of younger participants between the ages of 18 and 49 years. For this study, we restricted our sample to those aged 50 years and older. Figure 1. Open in new tabDownload slide The geographic location of the study communities in the six countries. Figure 1. Open in new tabDownload slide The geographic location of the study communities in the six countries. Measurement of Hand-Grip Strength Hand-grip strength was measured in both hands using a Smedley’s hand dynamometer (16). Participants who had any previous surgery on their arms, hands, or wrists during the last 3 months, or who had arthritis or pain in their hands or wrists, were excluded from the measurement. During the measurement, the study participants were seated in the upright position with their arm along their body; the arm was bent at 90° at the elbow with the forearm and wrist in the neutral position. Hand-grip strength (in kilograms, kg) was measured two times in each hand with brief pauses between each measurement. The highest of the four measurements was considered the maximum hand-grip strength and was used in the subsequent analysis. Air Pollution Assessment For this study, the domestic fuel type and ventilation apparatus for cooking were used as indicators of indoor air pollution. Two fuel types were mainly used for domestic cooking: solid fuels, such as coal, wood, dung, and agricultural residues; and electricity, liquid and gas fuels (including liquefied petroleum gas and natural gas). Respondents were also asked whether there was an indoor ventilation apparatus in the area where cooking was done (chimney, extraction hood, fan, or none) (17). Ambient PM2.5 was used as the indicator for outdoor air pollution, which was estimated using a global satellite-derived PM2.5 estimation (18). To measure the light extinction due to aerosol, aerosol optical depth data were retrieved from the Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging Spectroradiometer instruments from the Terra satellite. By combining aerosol optical depth data with vertical profiles of aerosol data from a chemical transport model (19), we can estimate ground-level PM2.5 concentrations. The community locations in SAGE were geo-coded and linked with the estimated PM2.5 concentrations. We used the average PM2.5 for the 3 years immediately preceding the survey as the independent variable in the main models. As one validation, we compared the monitored PM2.5 concentrations (measured by beta ray attenuation method (20)) with the satellite estimated PM2.5 concentrations in China and observed that the estimated PM2.5 concentrations closely represented the actual monitoring measurements (with an R square of 0.80, as shown in Supplementary Figure 1). Covariates A series of covariates were considered in this study, including individual-level variables and demographic, socioeconomic, occupational, and lifestyle factors. Individual-level variables included age, sex, body mass index (BMI), marital status, smoking status and amount, alcohol consumption, physical activity, education, and household income. Marital status was classified into two broad groups: married and unmarried. Those who were either never married, separated, divorced, or widowed were classified as unmarried. Occupations were classified as air pollution related occupations (including mineral, construction, cleaning, renovation, mechanic-related work) (21), or not related to occupational air pollution exposure (such as administrative, office work, service, academic, sales, fishery, unemployed, etc.). Lifetime tobacco consumption was assessed in terms of smoking status (smoker or not), and the average amount of tobacco products or cigarette equivalents consumed per day. The Global Physical Activity Questionnaire was used to measure the intensity, duration, and frequency of physical activity (22). Three levels of physical activity (low, moderate, and high levels) were classified based on the participants’ responses to the questions about moderate or vigorous physical activities during work, transport activities to and from places, and recreational/leisure time activities. Each type of activity was categorized into low, moderate, or high levels, according to the time spent on each activity and its total energy requirement in metabolic equivalents. At the country level, we considered a few covariates. Gross domestic product per capita was obtained from the Central Intelligence Agency’s World Factbook. Percentage of population living in urban areas and per capita health care expenditure were retrieved from the World Bank’s World Development Indicators. Statistical Analysis The hand-grip strength of participants in the same communities may be dependent on each other, violating the independence assumption of regression models. We therefore applied a three-level linear mixed regression model with participants as the first-level units, the community as the second-level unit, and the country as the third-level unit (23,24). We selected covariates in the final models based on two criteria: (i) variables are known or hypothesized to be risk factors for lower hand-grip strength and (ii) univariate model analyses showed an association with the hand-grip strength (25). Variables that were associated with the outcome were included in the final multivariate models (Supplementary Table 1). Other important covariates, such as age, sex, and smoking status were also included in the models, even if they were not statistically significant in the univariate models. Therefore, Model 1 for both PM2.5 and indoor air pollution exposures included age, sex, BMI, education, marital status, occupational air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey. Model 2 for PM2.5 further included fuel type and ventilation, and Model 2 for fuel type and ventilation further included PM2.5. The estimated associations were expressed as absolute differences in hand-grip strength associated with both indoor and outdoor air pollution. For outdoor air pollution, we calculated the associations for per 10 μg/m3 increase in ambient PM2.5 concentrations. For indoor air pollution, we used “electricity/liquid/gas fuel type” and “without ventilation apparatus” as the reference, respectively, and examined the associations with solid fuel type and using a chimney or hood as the ventilation. We also constructed one composite indicator to combine these two variables: (i) electricity/liquid/gas fuel + ventilation (as reference), (ii) electricity/liquid/gas fuel + no ventilation, (iii) solid fuel + ventilation, and (iv) solid fuel + no ventilation. To examine potential effect modifiers, stratified analyses were performed using the following categorical variables: sex (men and women), age group (younger than 65 years vs 65 years and older), smoking status (nonsmokers vs ever-smokers), urbanity (urban vs rural), education (high vs low), and household income (high vs low). The statistical difference of the associations between the subgroups was examined by including an interactive term of air pollution and the potential effect modifier in the model (26). A series of sensitivity analyses was conducted. First, additional country-level covariates were incorporated into the models to control for potential confounding at the country level, specifically, we considered the gross domestic product per capita, the percentage of urban population, health care expenditure per capita, and the Gini coefficient (27). Second, we included the city variable in the second level to control for the unmeasured city-level characteristics. Third, indicators were assessed for different lengths of exposure by using average PM2.5 concentrations for 1, 2, 4, and 5 years before the survey. Fourth, we also used the annual PM2.5 concentrations estimated from the spatial resolution of 1 * 1 km as the exposure, which were derived from the annual PM2.5 concentrations estimated at a 10 ∗ 10 km resolution. Combined with local meteorological parameters and land use information, the estimated PM2.5 concentrations were then down-scaled into a 1 ∗ 1 km resolution (28). For the missing values of hand-grip strength and some important covariates of interest (such as BMI, household income, education level), we compared the distributions between the participants with and without missing data. We also performed an imputation to replace the missing values and conducted the regression model. To exclude the impacts of height, we also used the ratio between hand-grip strength and height as the dependent variable in the analysis. All the analyses were conducted using R version 3.2.2. In all analyses, a p-value <.05 was considered statistically significant. Results A total of 40,583 participants aged 50 years and older were initially contacted, among which, 36,742 agreed to participate in this survey, resulting in a response rate of 90.5%. Among them, 5,484 participants did not have a valid measurement of hand-grip strength, and the other 49 had missing values for age, sex, or other important covariates. The remaining 31,209 participants were included in this analysis. Though statistically significant, there were generally comparable characteristics between the participants included in the analysis and those excluded (Supplementary Table 2), including similar exposure to PM2.5 (23.3 and 23.6 μg/m3), and similar BMI (24.6 and 26.1 kg/m2), indicating a representative sample of the participants included in this analysis. Table 1 summarizes the sample sizes and mean ages, plus PM2.5 characteristics by country. Among the 31,209 participants from the six countries, the mean age was 63 years. The 3-year mean PM2.5 concentration in the six countries was 23.33 μg/m3. South Africa had the lowest level of PM2.5 with an annual concentration of 6.00 μg/m3; China and India had the highest PM2.5 concentrations at 32.32 and 31.08 μg/m3, respectively. Table 1. Description of Population and Air Pollution Characteristics, by Country, SAGE Wave 1 (2007–2010) Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Note: SD = standard deviation; min = minimum; max = maximum. *The 3 years’ average concentration of PM2.5 was used. Open in new tab Table 1. Description of Population and Air Pollution Characteristics, by Country, SAGE Wave 1 (2007–2010) Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Country Participants (n) Age, Years, Mean (SD) PM2.5 (μg/m3)* Min Mean Median Max China 12,320 63 (9) 10.66 32.32 30.52 55.53 Ghana 4,115 64 (11) 12.21 17.50 17.45 22.79 India 6,361 62 (9) 7.86 31.08 27.42 64.08 Mexico 1,923 68 (9) 3.75 10.80 11.14 17.03 Russia 3,120 64 (10) 2.32 6.13 6.19 16.90 South Africa 3,370 63 (10) 1.50 6.00 5.92 20.55 SAGE overall 31,209 63 (10) 1.50 23.33 18.42 64.08 Note: SD = standard deviation; min = minimum; max = maximum. *The 3 years’ average concentration of PM2.5 was used. Open in new tab Supplementary Table 3 provides more details about the general characteristics of the participants, stratified by sex. Among the 31,209 participants, 16,649 (53.35%) were women and 14,560 (46.65%) were men. The mean hand-grip strength was 29.11 kg, and relatively higher among men than women (34.50 vs 24.39 kg). The mean age was similar between men and women. Women had a higher BMI than men (25.34 kg/m2 for women and 23.68 kg/m2 for men). Men were more likely to have occupational air pollution exposure, to be married, to live in rural areas, have higher household income, and to report smoking or drinking. A higher percentage of men used solid fuels, but a higher proportion used ventilation. Table 2 shows the associations between exposure to PM2.5, indoor air pollution (fuel type and ventilation), and hand-grip strength in the univariate and multivariate models. Comparable estimates were observed for the average concentration of PM2.5 over different years. We presented and conducted the subsequent analyses based on the 3 years’ average concentration. In the univariate regression model, each 10 μg/m3 increase in the 3 years’ average concentration of ambient PM2.5 was associated with a 0.70 kg (95% CI: −1.26, −0.14) lower hand-grip strength. Similar estimates were obtained in the multivariate models controlling for various factors, with (Model 2) or without (Model 1) fuel type and ventilation adjusted. Compared with electricity/liquid/gas fuel users, those using solid fuels indoors had relatively lower levels of hand-grip strength (β = −1.25, 95% CI: −1.74, −0.75), and the adjusted estimates were −0.90 (95% CI: −1.39, −0.40) without ambient PM2.5 in the multivariate model (Model 1) and −0.86 (95% CI: −1.35, −0.37) in the multivariate model including ambient PM2.5 (Model 2). We did not find statistically an association with ventilation in either the univariate or multivariate models. The results of the composite indicator (Supplementary Table 4) showed that, compared with the reference group (electricity/liquid/gas and ventilation), there was a negative association of the combination of solid fuel and adequate ventilation (β = −0.86, 95% CI: −1.33, −0.35), and the combination of solid fuel and no ventilation (β = −0.77, 95% CI: −1.31, −0.24). However, we did not find any association in the electricity/liquid/gas fuel and no ventilation group. Table 2. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution, SAGE Wave 1 (2007–2010) Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Note: CI = confidence interval. *Model 1: adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey. †Model 2 further adjusted for fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type and ventilation). Open in new tab Table 2. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution, SAGE Wave 1 (2007–2010) Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Crude Estimates 95% CI Model 1* Model 2† Adjusted Estimates 95% CI Adjusted Estimates 95% CI Ambient PM2.5  1 y average −0.63 −1.19, −0.06 −0.69 −1.25, −0.12 −0.68 −1.24, −0.11  2 y average −0.72 −1.30, −0.14 −0.78 −1.36, −0.21 −0.77 −1.35, −0.20  3 y average −0.70 −1.26, −0.14 −0.89 −1.43, −0.35 −0.86 −1.41, −0.32  4 y average −0.73 −1.29, −0.16 −0.90 −1.45, −0.35 −0.88 −1.43, −0.34  5 y average −0.66 −1.23, −0.09 −0.85 −1.40, −0.29 −0.83 −1.38, −0.28 Fuel type  Electricity/liquid/gas 1.00 1.00 1.00  Solid −1.25 −1.74, −0.75 −0.90 −1.39, −0.40 −0.86 −1.35, −0.37 Ventilation  Without 1.00 1.00 1.00  Chimney 0.02 −0.81, 0.85 0.29 −0.42, 1.00 0.19 −0.52, 0.91  Hood 0.82 −0.20, 1.85 0.44 −0.43, 1.31 0.32 −0.55, 1.20 Note: CI = confidence interval. *Model 1: adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey. †Model 2 further adjusted for fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type and ventilation). Open in new tab Table 3 illustrates the associations in the stratified analyses. For the associations with outdoor PM2.5, participants living in urban areas and having a higher education level had higher associations, each 10 μg/m3 increase in ambient PM2.5 corresponded to 2.27 kg (95% CI: −3.47, −1.07) and 3.15 kg (95% CI: −4.55, −1.76) lower hand-grip strength, respectively; while the corresponding estimates were −0.46 kg (95% CI: −1.00, 0.08) and −0.71 kg (95% CI: −1.24, −0.18) among the rural residents and those with a lower educational level. We did not find effect modifications by sex, age group, smoking status, and household income on the association. Table 3. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution: Results From the Stratified Analyses Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Note: CI = confidence interval. p value is for the difference among the strata. For fuel type, we reported the effect of solid fuels on hand-grip strength compared with electricity, liquid and gas fuels. Adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey, as well as fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type). *The 3 years’ average concentration of PM2.5 was used. Open in new tab Table 3. Estimated Absolute Difference in Hand-Grip Strength (unit: kg) With 95% CI Associated With Long-Term Exposure to PM2.5 and Indoor Air Pollution: Results From the Stratified Analyses Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Factors Ambient PM2.5* Fuel Type β 95% CI p β 95% CI p Sex  Men −0.80 −1.38, −0.22 −1.08 −1.82, −0.34  Women −0.82 −1.38, −0.27 .24 −0.86 −1.49, −0.22 .19 Age group  <65 y −0.84 −1.41, −0.27 −1.21 −1.91, −0.52  ≥65 y −0.82 −1.38, −0.27 .23 −0.76 −1.56, 0.05 .05 Height  ≤1.6 m −0.73 −1.25, −0.21  >1.6 m −0.80 −1.40, −0.20 .12 .07 Smoking  Nonsmokers −0.82 −1.38, −0.25 −0.82 −1.48, −0.16  Ever-smokers −0.77 −1.37, −0.17 .23 −1.16 −2.00, −0.31 .14 Urbanity  Urban −2.27 −3.47, −1.06 −0.60 −1.36, 0.15  Rural −0.46 −1.00, 0.08 <.01 −1.24 −1.89, −0.58 .04 Education  High −3.15 −4.55, −1.76 −0.98 −2.06, 0.11  Low −0.71 −1.24, −0.18 <.01 −0.96 −1.52, −0.41 .24 Household income  High −0.86 −1.46, −0.26 −0.30 −1.01, 0.40  Low −0.86 −1.43, −0.30 .23 −1.14 −1.83, −0.46 <.01 Note: CI = confidence interval. p value is for the difference among the strata. For fuel type, we reported the effect of solid fuels on hand-grip strength compared with electricity, liquid and gas fuels. Adjusted for age, sex, body mass index (BMI), education, marital status, occupation air pollution exposure, smoking, alcohol drinking, physical activity, urbanity, household income, and season of the survey, as well as fuel type and ventilation (for ambient PM2.5) and ambient PM2.5 (for fuel type). *The 3 years’ average concentration of PM2.5 was used. Open in new tab For the associations with solid fuel usage, we found statistically significant modifications of age, urbanity, and household income (Table 3). For example, compared with electricity, liquid and gas fuels usage, solid fuels usage was associated with a lower hand-grip strength of 1.21 kg (95% CI: −1.91, −0.52) among participants younger than 65 years, and 0.76 kg (95% CI: −1.56, 0.05) among those aged 65 years and older (though the difference between younger and older participants was only marginally significant, p = .05). For rural residents the lower hand-grip strength was 1.24 kg (95% CI: −1.89, −0.58) and 0.60 kg (95% CI: −1.36, 0.15) among urban residents. Those with a lower household income had a lower hand-grip strength of 1.14 kg (95% CI: −1.83, −0.46) while those of higher household income had a lower hand-grip strength of 0.30 kg (95% CI: −1.01, 0.40). The differences in the estimated associations were not statistically significant by sex, smoking status, and education levels. The sensitivity analyses using different years’ concentrations as the exposure variable produced consistent estimates (Table 1 and Supplementary Table 5). For example, each 10 μg/m3 increase in PM2.5 concentration was associated with lower hand-grip strength (β = −0.68, 95% CI: −1.24, −0.11 for 1-year PM2.5). When country-level covariates were included in the model, each 10 μg/m3 increase in PM2.5 was associated with a 0.75 kg lower hand-grip strength (95% CI: −1.29, −0.21). The models with the city variable in the second level also produced consistent results. For example, each 10 μg/m3 increase in PM2.5 corresponded to a 0.88 kg lower hand-grip strength (95% CI: −1.66, −0.10). The analyses based on the 1 * 1 km estimated PM2.5 also yielded comparable estimates. When replacing the missing values by imputation, we also obtained a consistent association. For example, each 10 μg/m3 increase in PM2.5 was associated with a 0.85 kg lower hand-grip strength (95% CI: −1.40, −0.31) using the complete data set. When using the ratio between hand-grip strength and height as the dependent variable, we also obtained statistically significant associations with both ambient PM2.5 and indoor air pollution. Discussion Our study showed that both indoor air pollution from solid fuel burning and ambient PM2.5 were associated with lower hand-grip strength. However, ventilation was not found to be associated with hand-grip strength. Additionally, our study found that participants living in urban areas and having a higher education level were more susceptible to the impacts of ambient PM2.5. Furthermore, participants younger than 65 years, rural participants, and those with lower household income were more susceptible to the impacts of indoor air pollution resulting from solid fuel usage. The linkage of indoor and outdoor air pollution with hand-grip strength has not been well studied, though occupational exposures to pesticides, solvents, and some heavy metals have been associated with lower hand-grip strength (29), and both indoor and outdoor air pollution share some chemical components with occupational setting exposure. In this sense, the observed associations in this study were biologically plausible. One recent study suggested that air pollution was associated with age-related frailty, which was in line with our findings (30). Air pollution has been associated with nervous system-related health outcomes (27). Hand-grip strength has been closely related to the health of the nervous system, and a decline in hand-grip strength has been suggested to be one predictor of nervous impairment (3). It was possible that some components of the air pollutants could impair the nervous system (31,32), which subsequently affected hand-grip strength. The fine particles were able to pass through the throat and nose, and ultimately enter into the circulatory system and brain, and thus resulted in adverse nervous effects, such as stroke, and neurodegenerative diseases (33). This idea has been supported by both human and animal studies (34,35). For example, epidemiological studies have reported that long-term exposure to ambient PM2.5 was associated with increased risk of stroke (27,36). Additionally, the nerve cells of the brain are particularly sensitive to the impacts of particulate pollution exposure (37). Air pollutants could adversely affect the nervous system directly (after penetrating into the brain) or indirectly (through the inflammatory processes and oxidative stress) (38). For indoor air pollution, though this study observed an association with fuel type, ventilation was not found to be associated with hand-grip strength. Chimneys were mainly used for ventilation in rural areas. It was possible that indoor air pollution was still very high in the presence of chimneys, while in urban areas, electricity/liquid/gas fuels and vent hoods were mainly used. Electricity/liquid/gas fuels were related with relatively higher hand-grip strength compared with solid fuels. It was thus understandable that vent hoods were not related to hand-grip strength. Urbanity and education were found to be important effect modifiers of the association between ambient PM2.5 and hand-grip strength. The higher associations among urban residents and those with a higher education level might be associated with the chemical composition and emission source of the particulate pollution. There might be more hazardous components in the particles of the urban areas (39), while the particulate pollution in the rural areas may be more related to natural sources, and thus potentially be less harmful (40). The other possibility might be that air pollution cannot disperse as quickly in urban areas due to the high buildings (41), the urban residents were thus likely to expose to higher levels of air pollution. The higher exposure level among urban residents may also help explain the results of the higher association between the more highly educated participants and lower hand-grip strength, as among the participants in this study, 43% of urban residents were highly educated, compared to only 14% in rural areas. We did not observe a sex differential association between indoor air pollution and hand-grip strength. This was somewhat surprising given that women were more likely to do most of cooking at home. This result must be interpreted cautiously. One possible reason might be the lack of adjustment for environmental tobacco smoking in the model, resulting in residual confounding. It is also possible that women were more adapted to the indoor air pollution due to their longer exposures, and less vulnerable to the impacts of indoor air pollution exposure. Further studies are needed to test this speculation using the follow-up data from the SAGE Wave 2 study. The higher association of indoor air pollution from solid fuel combustion with hand-grip strength among younger participants (50–64 years) than older adults (≥65 years) might indicate that the younger adults may have mainly participated in the domestic cooking, causing them to have more intensive and longer exposures than those over age of 65 years. It is also possible that the hand-grip strength among the older participants remained relatively more stable than the younger adults and less sensitive to the impacts of indoor air pollution; however, more studies are needed to investigate this possibility. Smokers were found to be more vulnerable to the impacts of indoor air pollution from solid fuel usage. It is possible that smoking could decrease the clearance, and increase the deposition and retention of air pollutants, and thus enhance the impacts of indoor air pollution (42). The higher associations in rural areas could be associated with the higher indoor air pollution (43) and higher rates and consumption of smoking in rural areas (44). This is also one possible reason for the higher association among those with lower household income observed in this study. This study possessed a few strengths. To our knowledge, this is the first effort to simultaneously investigate the associations of both indoor and outdoor air pollution with hand-grip strength. Additionally, this study covered a population with a wide range of different characteristics in the six low- and middle-income countries represented in the SAGE study. More importantly, we collected and controlled for a series of important potential confounders in this analysis, and obtained a robust association between air pollution and hand-grip strength. A few limitations should also be noted. As a cross-sectional study, we cannot establish a causal relationship between air pollution and hand-grip strength. Selection bias was possible, especially due to the exclusion of the subjects with existing illnesses of the arms, hands, or wrists from the analysis. Furthermore, we did not have data on long-term air pollution exposures, though our measure may reflect some degree of long-term air pollution exposures. We used ambient PM2.5 of the centroid of the community as an indicator of outdoor air pollution due to data limitation (we did not have the individual addresses of each participant), as we only had the annual concentration of PM2.5. It was hard for us to conduct an exposure assessment before the survey; we thus used the 3 years’ average concentration before the assessment year as an indicator of exposure. This would introduce non-differential exposure misclassification, and bias the results toward the null (27). However, considering the spatial correlation of annual PM2.5 of the residents within a community, we regard that such a bias would not play an important role in this study. Our sensitivity analysis using exposure assessment from 1 * 1 km resolution showed consistent estimates, indicating that spatial misalignment had little impact on the results if any. Further, domestic fuel type and ventilation were crude proxies for indoor air pollution, and we were unable to collect more information, such as the use of both electricity/liquid/gas and solid fuel types in the same household, duration of cooking, or housing type to refine the indoor air pollution exposure. It is not practical to measure air pollution exposures using personal air monitoring devices for such a large-scale epidemiologic study. Additionally, we cannot exclude the possibility of influence of some potential confounding factors, such as environmental tobacco smoking, meteorological factors, and other air pollutants, which might have resulted in residual confounding. In conclusion, this study adds to the evidence that exposure to ambient PM2.5 and indoor air pollution from domestic solid fuels usage is associated with lower hand-grip strength. Additionally, the study shows that more stringent air pollution control measures should be formulated to protect the public’s health. Funding This work was in part supported by the National Key R&D Program of China (grant no: 2018YFA0606200) and Foundation of Shanghai Municipal Commission of Health and Family Planning (general program: 201640148). Acknowledgments We thank all the participants and investigators in this survey. 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Journal

The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences – Oxford University Press

Published: Jan 20, 2020

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